Method, system, device and medium for evaluating heat insulation performance of heat insulation sponge

Abstract

The present application relates to a method, system, device and medium for evaluating the heat insulation performance of heat-insulating sponges, belonging to the technical field of performance evaluation of heat-insulating materials. The performance evaluation method includes: obtaining the initial parameters of the heat-insulating sponge sample to be detected; receiving and storing the temperature-heat transfer curve of the heat-insulating sponge sample, generating the temperature-thermal conductivity curve of the heat-insulating sponge sample, and calculating the heat diffusion coefficient data; receiving the heat map data, and calibrating the temperature response based on the heat diffusion coefficient to generate a temperature distribution matrix; calculating the temperature data of different depth layers inside the heat-insulating sponge sample; receiving the high and low temperature heat insulation attenuation curve obtained from the high and low temperature cycle test of the heat-insulating sponge sample; receiving the humidity-temperature heat insulation attenuation data set obtained from the humidity-temperature cycle test of the heat-insulating sponge sample; and obtaining the heat insulation performance data according to the high and low temperature heat insulation attenuation curve and the humidity-temperature heat insulation attenuation data set. The present application can accurately evaluate the heat insulation performance of heat-insulating sponge materials.

Description

Technical Field

[0001] The present application relates to the technical field of thermal insulation material performance evaluation, and in particular to a thermal insulation performance evaluation method, system, equipment and medium for thermal insulation sponge. Background Art

[0002] As an important thermal insulation material, thermal insulation sponge is widely used in construction, industrial equipment, transportation and other fields to achieve the goals of reducing heat loss, saving energy and improving safety of use. The thermal insulation performance of thermal insulation sponge mainly depends on its material structure and physical properties, such as porosity, pore size, material thickness, etc. These factors directly affect the thermal conductivity and thermal insulation effect of the material. However, since thermal insulation materials often need to face complex environmental conditions in actual applications, such as drastic temperature fluctuations and high humidity, the thermal insulation effect of thermal insulation sponge often decays after long-term use or in specific environments.

[0003] At present, the existing evaluation methods for thermal insulation materials mainly use basic physical parameters such as heat transfer and thermal conductivity under a single temperature difference to preliminarily judge the thermal insulation performance of the material. However, such evaluation methods often ignore the changes in the thermal insulation performance of the material under the superposition of multiple environmental factors, and are unable to evaluate the thermal insulation attenuation of the material under different environments.

[0004] In practical applications, the long-term thermal insulation performance of thermal insulation materials is particularly critical, especially in environments with large temperature differences. The sustainability of the thermal insulation effect of the material is directly related to the energy-saving effect and safety of its application. Therefore, how to accurately and comprehensively evaluate the thermal insulation performance of thermal insulation sponge materials under a variety of complex environmental conditions is a technical problem that needs to be solved urgently. Summary of the invention

[0005] In order to accurately evaluate the thermal insulation performance of thermal insulation sponge materials, the present application provides a thermal insulation performance evaluation method, system, equipment and medium for thermal insulation sponge.

[0006] In the first aspect, the present application provides a method for evaluating the thermal insulation performance of a thermal insulation sponge, using the following technical solution:

[0007] A method for evaluating the thermal insulation performance of a thermal insulation sponge, the performance evaluation method comprising:

[0008] Obtaining initial parameters of the thermal insulation sponge sample to be tested; wherein the initial parameters include sample thickness, sample surface area, pore density and pore size of the thermal insulation sponge sample;

[0009] Receive and store the heat transfer data of the thermal insulation sponge sample under different temperature difference conditions to obtain a temperature-heat transfer curve of the thermal insulation sponge sample;

[0010] Based on the temperature-heat transfer curve and the sample thickness, the temperature-thermal conductivity curve of the thermal insulation sponge sample is generated through the steady-state heat flow formula, and the thermal diffusivity data is calculated;

[0011] Receive the thermal map data of the surface temperature distribution of the thermal insulation sponge sample, and calibrate the temperature response based on the thermal diffusivity to generate a temperature distribution matrix;

[0012] Based on the temperature distribution matrix and the thermal diffusivity, calculate the temperature data of different depth layers inside the thermal insulation sponge sample to generate a temperature grid data set;

[0013] Receive the temperature change data obtained from the high and low temperature cycle test of the thermal insulation sponge sample, and combine with the initial temperature distribution in the temperature grid data set to obtain the high and low temperature thermal insulation attenuation curve;

[0014] Receive the temperature change data and humidity change data obtained from the humidity-temperature cycle test of the thermal insulation sponge sample, compare them with the high and low temperature thermal insulation attenuation curve, and obtain the humidity-temperature thermal insulation attenuation data set according to the comparison result;

[0015] Conduct an analysis of the thermal insulation attenuation trend based on the high and low temperature thermal insulation attenuation curve and the humidity-temperature thermal insulation attenuation data set to obtain the thermal insulation performance data of the thermal insulation sponge sample.

[0016] By adopting the above technical solution, the temperature and humidity change data of the thermal insulation sponge sample to be detected are analyzed in a multi-level and all-round manner, which can not only quantify the thermal insulation attenuation trend of the sample under multiple conditions such as high and low temperature and humidity environment, but also provide an evaluation of the thermal insulation persistence and stability of the sample. This technical solution provides a scientific and data-driven reference system for the performance analysis of thermal insulation materials, making the selection, optimization and application of thermal insulation materials more reliable.

[0017] Optionally, the step of receiving the thermal map data of the surface temperature distribution of the thermal insulation sponge sample and calibrating the temperature response based on the thermal diffusivity to generate a temperature distribution matrix includes:

[0018] Receive the thermal map data of the surface temperature distribution of the thermal insulation sponge sample;

[0019] Calculate the temperature response delay time on the surface of the thermal insulation sponge sample based on the thermal diffusivity;

[0020] Adjust the temperature amplitude in the thermal map data based on the temperature response delay time to calibrate each temperature value in the thermal map data;

[0021] Arrange the calibrated temperature values into a temperature distribution matrix according to the original position information of the thermal map data;

[0022] Quantify the calibration accuracy of the temperature distribution matrix based on a statistical method, and verify the temperature distribution matrix according to the calibration accuracy.

[0023] Optionally, based on the temperature distribution matrix and the thermal diffusion coefficient, the steps of calculating the temperature data of different depth layers inside the thermal insulation sponge sample and generating a temperature grid data set include:

[0024] Determine the number of depth layers according to the sample thickness and the preset layer spacing of the thermal insulation sponge sample to obtain a depth layer list;

[0025] Based on the temperature distribution matrix and the thermal diffusion coefficient, calculate the temperature of the first layer in the depth layer list according to the heat transfer equation;

[0026] Based on the temperature of the first layer in the depth layer list, calculate the temperature of each layer in the depth layer list in turn according to the heat transfer formula to obtain the temperature data of all depth layers;

[0027] Arrange the temperature data of all depth layers according to the depth layer list to construct a temperature grid data set.

[0028] Optionally, the steps of receiving the temperature change data obtained from the high-low temperature cycling test of the thermal insulation sponge sample and combining it with the initial temperature distribution in the temperature grid data set to obtain a high-low temperature heat insulation attenuation curve include:

[0029] Receive the temperature change data obtained from the high-low temperature periodic cycling test of the thermal insulation sponge sample;

[0030] Based on the initial temperature distribution in the temperature grid data set, normalize the temperature change data obtained from each temperature cycling test;

[0031] Arrange the normalized temperature change data according to the depth layer list to obtain a temperature change matrix of different depth layers;

[0032] Based on the temperature change matrix, calculate the temperature attenuation rate of each depth layer;

[0033] Arrange the temperature attenuation rates of each depth layer in each temperature cycling test in chronological order to obtain a high-low temperature heat insulation attenuation curve.

[0034] Optionally, the steps of receiving the temperature change data and humidity change data obtained from the humidity-temperature cycling test of the thermal insulation sponge sample, comparing them with the high-low temperature heat insulation attenuation curve, and obtaining a humidity-temperature heat insulation attenuation data set according to the comparison result include:

[0035] Receive the temperature change data and humidity change data obtained from the humidity-temperature periodic cycling test of the thermal insulation sponge sample;

[0036] Normalize the temperature change data and the humidity change data;

[0037] Arrange the normalized temperature change data and humidity change data according to the depth layer list to obtain a humidity-temperature change matrix for different depth layers;

[0038] Based on the humidity-temperature change matrix, calculate the temperature decay rate for each depth layer to generate a corresponding temperature decay rate matrix;

[0039] Compare the temperature decay rates in the temperature decay rate matrix layer by layer with the temperature decay rates in the high and low temperature heat insulation decay curve to obtain a comparison result;

[0040] Based on the comparison result, establish a humidity-temperature heat insulation decay data set.

[0041] Optionally, the steps of analyzing the heat insulation decay trend according to the high and low temperature heat insulation decay curve and the humidity-temperature heat insulation decay data set to obtain the heat insulation performance data of the heat insulation sponge sample include:

[0042] Normalize the high and low temperature heat insulation decay curve and the humidity-temperature heat insulation decay data set;

[0043] Compare the normalized high and low temperature heat insulation decay curve and the humidity-temperature heat insulation decay data set layer by layer to obtain a difference matrix;

[0044] Based on the difference matrix, calculate the heat insulation decay trend curve of the heat insulation sponge sample through a trend fitting algorithm to fit a heat insulation decay trend model;

[0045] Based on the heat insulation decay trend model, calculate the comprehensive heat insulation decay rate under high and low temperature conditions and humidity-temperature conditions;

[0046] According to the heat insulation decay trend model and the comprehensive heat insulation decay rate, obtain the heat insulation performance data of the heat insulation sponge sample.

[0047] Optionally, after the step of obtaining the humidity-temperature heat insulation decay data set according to the comparison result, it further includes:

[0048] Input the high and low temperature heat insulation decay curve and the humidity-temperature heat insulation decay data set into a pre-constructed finite element model to obtain a finite element microstructure model;

[0049] Based on the temperature grid data set, adjust the internal parameters of the finite element microstructure model to obtain a microstructure simulation model of the heat insulation sponge sample;

[0050] Adjust the pore density and pore size parameters in the microstructure simulation model, calculate the heat insulation performance data of the model according to each group of parameter combinations, and generate an optimized heat insulation performance data set;

[0051] Based on the optimized heat insulation performance data set, select the parameter combination with the best heat insulation performance data, and generate the structural optimization suggestion information of the heat insulation sponge sample.

[0052] In a second aspect, the present application provides a heat insulation performance evaluation system for a heat insulation sponge, adopting the following technical solution:

[0053] A heat insulation performance evaluation system for a heat insulation sponge, the performance evaluation system includes:

[0054] A parameter acquisition module for acquiring the initial parameters of the heat insulation sponge sample to be detected; wherein, the initial parameters include the sample thickness, sample surface area, pore density and pore size of the heat insulation sponge sample;

[0055] A first data processing module for receiving and storing the heat transfer amount data of the heat insulation sponge sample under different temperature difference conditions, and obtaining the temperature-heat transfer amount curve of the heat insulation sponge sample;

[0056] A second data processing module for generating the temperature-thermal conductivity curve of the heat insulation sponge sample based on the temperature-heat transfer amount curve and the sample thickness through the steady-state heat flow formula, and calculating the thermal diffusivity data;

[0057] A temperature distribution matrix generation module for receiving the heat map data of the surface temperature distribution of the heat insulation sponge sample, and calibrating the temperature response based on the thermal diffusivity to generate a temperature distribution matrix;

[0058] A temperature grid data set generation module for calculating the temperature data of different depth layers inside the heat insulation sponge sample based on the temperature distribution matrix and the thermal diffusivity, and generating a temperature grid data set;

[0059] A third data processing module for receiving the temperature change data obtained from the high and low temperature cycle test of the heat insulation sponge sample, and combining the initial temperature distribution in the temperature grid data set to obtain a high and low temperature heat insulation attenuation curve;

[0060] A fourth data processing module for receiving the temperature change data and humidity change data obtained from the humidity-temperature cycle test of the heat insulation sponge sample, and comparing them with the high and low temperature heat insulation attenuation curve, and obtaining a humidity-temperature heat insulation attenuation data set according to the comparison result;

[0061] The heat insulation performance data generation module is used to perform heat insulation attenuation trend analysis based on the high and low temperature heat insulation attenuation curve and the humidity-temperature heat insulation attenuation data set, so as to obtain the heat insulation performance data of the heat insulation sponge sample.

[0062] In a third aspect, the present application provides a computer device, adopting the following technical solution:

[0063] A computer device includes a memory, a processor, and a computer program stored on the memory. The processor executes the computer program to implement the steps of the method as described in the first aspect.

[0064] In a fourth aspect, the present application provides a computer-readable storage medium, adopting the following technical solution:

[0065] A computer-readable storage medium stores a computer program that can be loaded and executed by a processor to implement any one of the methods in the first aspect.

[0066] In summary, the present application includes at least one of the following beneficial technical effects: The present application provides a quantitative heat insulation performance analysis method for heat insulation materials in complex environments, which can not only support the optimal design of materials, but also be applied in fields such as construction and industry to ensure the stable heat insulation performance of materials in different environments. Through a scientific evaluation system and data guidance, it provides a reliable basis for material selection and research and development, and promotes the efficient application and long-term performance improvement of heat insulation materials. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 is the first process schematic diagram of the heat insulation performance evaluation method of the heat insulation sponge in one embodiment of the present application.

[0068] Figure 2 is the second process schematic diagram of the heat insulation performance evaluation method of the heat insulation sponge in one embodiment of the present application.

[0069] Figure 3 is the third process schematic diagram of the heat insulation performance evaluation method of the heat insulation sponge in one embodiment of the present application.

[0070] Figure 4 is the fourth process schematic diagram of the heat insulation performance evaluation method of the heat insulation sponge in one embodiment of the present application.

[0071] Figure 5 is the fifth process schematic diagram of the heat insulation performance evaluation method of the heat insulation sponge in one embodiment of the present application.

[0072] Figure 6 is the sixth process schematic diagram of the heat insulation performance evaluation method of the heat insulation sponge in one embodiment of the present application.

[0073] Figure 7 It is the seventh process schematic diagram of the heat insulation performance evaluation method of the heat insulation sponge in one embodiment of the present application. Specific implementation manners

[0074] In order to make the purpose, technical solutions and advantages of the present application clearer, the following further elaborates on the present application in conjunction with Figure 1-7 the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0075] In the field of heat insulation materials, the evaluation of heat insulation performance has always been a key link in material research and development and application. Traditional heat insulation performance evaluation methods usually only focus on the thermal conductivity and thermal diffusivity of materials under standard experimental conditions, and it is difficult to comprehensively reflect the actual performance of materials in complex environments. However, the usage environments of heat insulation materials often face various extreme conditions, such as high and low temperature cycles, humidity changes, etc. These environmental factors will have a significant impact on the heat insulation performance of materials, making their heat conduction and attenuation characteristics different in actual applications.

[0076] In addition, the microscopic structure of heat insulation materials (such as pore density, pore size) plays a crucial role in heat insulation performance, and it is difficult for traditional evaluation methods to deeply analyze the relationship between its microscopic structure and heat insulation performance. Therefore, how to dynamically evaluate the heat insulation performance of heat insulation materials under various environmental factors, analyze the microscopic structure characteristics of materials, and optimize the material structure to achieve the best heat insulation effect has become an important requirement in the current research of heat insulation material technology.

[0077] Based on this, the embodiments of the present application disclose a heat insulation performance evaluation method for a heat insulation sponge.

[0078] Referring to Figure 1 , a heat insulation performance evaluation method for a heat insulation sponge, the performance evaluation method includes:

[0079] Step S101, obtaining the initial parameters of the heat insulation sponge sample to be detected;

[0080] Among them, the initial parameters include the sample thickness, sample surface area, pore density and pore size of the heat insulation sponge sample;

[0081] Specifically, the initial parameters (thickness, pore density and pore size) of the sample are important basic parameters for heat insulation performance. The porosity and pore size directly affect the heat insulation ability of the material. These characteristics determine the distribution of air inside the material and its heat conduction path, which helps to improve the accuracy of subsequent data processing steps and ensure that the calculated thermal conductivity and thermal diffusion coefficient accurately reflect the true characteristics of the material.

[0082] Step S102: Receive and store the heat transfer data of the heat insulation sponge sample under different temperature difference conditions to obtain the temperature-heat transfer curve of the heat insulation sponge sample.

[0083] Among them, the heat insulation performance is usually measured by the change of heat transfer with the temperature difference. The temperature-heat transfer curve is used to describe the heat transfer situation of the sample under different temperature difference conditions and reflects the basic heat insulation behavior of the material.

[0084] Exemplarily, the sample is placed in environments with temperature differences of 10°C, 20°C, and 30°C, and the corresponding heat transfer amounts are measured as 0.1W, 0.2W, and 0.3W respectively. Thus, the corresponding temperature-heat transfer curve can be obtained, which is convenient for calculating the thermal conductivity of the material in the subsequent steps and laying a data foundation for the quantitative analysis of the heat insulation performance.

[0085] Step S103: Based on the temperature-heat transfer curve and the sample thickness, generate the temperature-thermal conductivity curve of the heat insulation sponge sample through the steady-state heat flow formula and calculate the thermal diffusivity data.

[0086] Among them, the thermal conductivity is a key indicator to measure the heat transfer ability of the material and describes the heat conduction rate of the material under stable temperature conditions. Specifically, the calculation of the thermal conductivity is usually based on the steady-state heat flow formula. By combining parameters such as temperature difference, heat transfer amount, and material thickness, the temperature-thermal conductivity curve can be generated, and the thermal diffusivity of the material can be further calculated using the sample thickness to characterize its transient heat conduction ability.

[0087] Step S104: Receive the thermal map data of the surface temperature distribution of the heat insulation sponge sample and calibrate the temperature response based on the thermal diffusivity to generate the temperature distribution matrix.

[0088] Among them, the thermal map data of the surface temperature distribution provides the temperature distribution map of the sample under heating conditions. By calibrating the thermal map data with the thermal diffusivity, the delay and amplitude of the temperature response are adjusted to generate the temperature distribution matrix of the sample to accurately reflect its surface temperature distribution.

[0089] Exemplarily, assuming that the thermal map data shows that the temperature distribution at different points on the sample surface is [30°C, 32°C, 34°C], after calibration with the thermal diffusivity, the temperature distribution matrix data is adjusted to [29.8°C, 32.1°C, 34.2°C] to more accurately reflect the true heat insulation effect of the material and provide reliable surface layer data for further analyzing the temperature distribution at different depths.

[0090] Step S105: Based on the temperature distribution matrix and the thermal diffusivity, calculate the temperature data of different depth layers inside the heat insulation sponge sample to generate the temperature grid data set.

[0091] Among them, according to the surface temperature distribution and the thermal diffusivity, the temperature data at different depths inside the sample are calculated layer by layer using the heat transfer equation, and a temperature grid dataset is gradually generated from the surface to the inside. This grid dataset shows the temperature gradient inside the sample.

[0092] Exemplarily, assuming that the surface temperature of the sample is 30°C, through layer-by-layer calculation using the thermal diffusivity, the temperatures at different internal depth layers (1 mm, 2 mm, 3 mm, etc.) are [29°C, 28°C, 27°C], thus obtaining a complete temperature grid dataset to accurately reflect the temperature distribution inside the sample, providing data support for the multi-level evaluation of the heat insulation effect and also providing a reference for the benchmark temperature of subsequent attenuation tests.

[0093] Step S106, receive the temperature change data obtained from the high-low temperature cycling test of the heat insulation sponge sample, and combine it with the initial temperature distribution in the temperature grid dataset to obtain the high-low temperature heat insulation attenuation curve;

[0094] Among them, the high-low temperature cycling test data record the temperature changes of the heat insulation sponge sample in a cyclic temperature difference environment. By combining the temperature change data with the temperature grid dataset, a high-low temperature heat insulation attenuation curve can be generated based on the temperatures at different depth layers, so as to facilitate the analysis of the heat insulation performance attenuation of the material under cyclic temperature difference conditions, revealing the stability of its heat insulation performance and providing a reference for the durability of the heat insulation material under extreme temperatures.

[0095] Exemplarily, assuming that the initial temperature grid of the heat insulation sponge sample in the high-low temperature cycling test is [30°C, 29°C, 28°C], after multiple cycles, the change rate of the internal temperature grid slows down, thus forming a temperature attenuation curve.

[0096] Step S107, receive the temperature change data and humidity change data obtained from the humidity-temperature cycling test of the heat insulation sponge sample, and compare them with the high-low temperature heat insulation attenuation curve. According to the comparison results, obtain the humidity-temperature heat insulation attenuation dataset;

[0097] Among them, since humidity affects the heat conduction performance of the material, by comparing the humidity-temperature cycling test data with the high-low temperature attenuation curve, the influence of humidity change on the heat insulation performance of the material can be observed, and the humidity-temperature heat insulation attenuation dataset can be obtained to further evaluate the performance stability of the material in a humid environment.

[0098] Exemplarily, the temperature attenuation rate of the sample may accelerate in a high-humidity environment. Assuming that the comparison curve shows that a 5% decrease in the heat insulation performance of the sample will occur when the humidity increases, the humidity-temperature heat insulation attenuation data can be generated accordingly to reflect the heat insulation durability of the material at different humidities and provide support for material selection in humidity-sensitive environments.

[0099] Step S108: Conduct heat insulation attenuation trend analysis based on the high and low temperature heat insulation attenuation curve and the humidity-temperature heat insulation attenuation data set to obtain the heat insulation performance data of the heat insulation sponge sample.

[0100] Specifically, based on the comparison between the high and low temperature heat insulation attenuation curve and the humidity-temperature heat insulation attenuation data set, the attenuation trend algorithm can be used to calculate the heat insulation attenuation trend of the material, and thus the heat insulation performance data of the sample can be obtained to quantify the heat insulation performance attenuation of the sample under various environmental factors, providing a scientific basis for the stability and applicability of the material in long-term heat insulation applications. For example, after calculating the attenuation trend, the comprehensive attenuation rate of the heat insulation performance of the heat insulation sponge sample is obtained as 0.8% / year, and this data reflects the long-term heat insulation stability of the heat insulation sponge.

[0101] In the above embodiments, the temperature and humidity change data of the heat insulation sponge sample to be detected are analyzed in a multi-level and all-round manner, which can not only quantify the heat insulation attenuation trend of the sample under multiple conditions such as high and low temperature and humidity environments, but also provide an evaluation of the heat insulation persistence and stability of the sample. This technical solution provides a scientific and data-driven reference system for the performance analysis of heat insulation materials, making the selection, optimization and application of heat insulation materials more reliable.

[0102] As an embodiment of obtaining the thermal diffusivity data, the specific steps include;

[0103] Step S1031: Based on the temperature-thermal conductivity curve and the surface area data of the heat insulation sponge sample, calculate the heat flux density q under steady state conditions according to the following formula (1);

[0104] (1);

[0105] Where Q represents the heat transfer amount, and A represents the sample surface area;

[0106] Step S1032: Based on the heat flux density and the temperature-thermal conductivity curve, calculate the thermal conductivity data k according to the following steady state heat flux formula (2);

[0107] (2);

[0108] Where d represents the sample thickness, and ΔT represents the temperature difference;

[0109] Based on the thermal conductivity data and the sample thickness data, calculate the thermal diffusivity α according to the following formula (3):

[0110] (3);

[0111] Where ρ is the sample density (i.e., the apparent density, which can be calculated according to the mass and volume of the heat insulation sponge sample); c pis the specific heat capacity of the sample, which can be obtained by querying the material database according to the composition of the heat-insulating sponge sample.

[0112] Referring to Figure 2 , as an implementation manner of step S103, the steps of receiving the thermal map data of the surface temperature distribution of the heat-insulating sponge sample and calibrating the temperature response based on the thermal diffusion coefficient to generate a temperature distribution matrix include:

[0113] Step S201, receiving the thermal map data of the surface temperature distribution of the heat-insulating sponge sample;

[0114] Among them, the surface of the heat-insulating sponge sample will present different temperature distributions under heating conditions. The thermal map data is usually collected by an infrared imaging device and used to measure the temperature values at different positions on the sample surface. These original thermal map data represent the uncalibrated temperature distribution. Due to the influence of thermal diffusion, these data may contain time response delays and amplitude errors. Assuming that the temperature readings of the thermal map data at different points on the sample surface are [30°C, 32°C, 34°C], these values correspond to the temperatures of different surface coordinates respectively.

[0115] Step S202, calculating the temperature response delay time on the surface of the heat-insulating sponge sample based on the thermal diffusion coefficient;

[0116] Specifically, the delay time of the temperature response is the time difference for the temperature to reach each position on the material surface. This delay depends on the thermal diffusion coefficient of the material, which characterizes the speed of heat conduction in the material. The thermal diffusion coefficient α is used to calculate the temperature response delay time t delay , which can reasonably calibrate the temperature change during the thermal diffusion process in terms of time. The calculation formula for the temperature delay time is:

[0117] (4);

[0118] Among them, d is the distance between two adjacent measurement points in the thermal map.

[0119] Step S203, adjusting the temperature amplitude in the thermal map data based on the temperature response delay time to calibrate each temperature value in the thermal map data;

[0120] Among them, the temperature amplitude calibration is based on the response delay time. By calibrating each temperature value in the thermal map data one by one, it is ensured that each temperature value is closer to the actual surface temperature. During the thermal diffusion process, the temperature response is usually delayed and weakened. Using the delay time to correct the temperature amplitude in the thermal map data can eliminate the temperature distortion caused by thermal diffusion.

[0121] Specifically, the calibration process mainly considers the influence of thermal diffusion on the temperature distribution, and the original temperature value in the thermal map can be adjusted by the following formula (5) , the calibrated temperature value It can be expressed as:

[0122] (5);

[0123] Wherein, is an adjusted value calculated based on the response delay time and the thermal diffusion coefficient, and is used to calibrate the actual temperature response.

[0124] Step S204: Arrange the calibrated temperature values into a temperature distribution matrix according to the original position information of the thermal map data;

[0125] Wherein, the calibrated temperature values are arranged in the coordinate order in the original thermal map data to form a temperature distribution matrix. The temperature distribution matrix is an accurate description of the temperature distribution on the material surface, and shows the temperatures at different positions on the sample surface in the form of a two-dimensional array, which is convenient for subsequent analysis.

[0126] Step S205: Quantify the calibration accuracy of the temperature distribution matrix based on statistical methods, and verify the temperature distribution matrix according to the calibration accuracy.

[0127] Wherein, statistical methods (such as mean square error, standard deviation, etc.) can be used to quantitatively analyze the temperature values in the temperature distribution matrix to evaluate the accuracy of the calibrated temperature distribution matrix. By comparing the temperature distribution differences before and after calibration, it can be verified whether the calibration effect meets the expectations and ensure the reliability of the data.

[0128] Specifically, the mean square error between the calibrated temperature value and the original measured temperature value can be calculated to quantify the calibration accuracy, and the standard deviation of the calibrated temperature distribution can be calculated to measure the uniformity of the temperature distribution.

[0129] In the above embodiments, the thermal map data of the heat-insulating sponge sample is received, the response delay time is calculated using the thermal diffusion coefficient, the thermal map data is accurately calibrated, and finally a high-precision temperature distribution matrix is generated and its calibration accuracy is verified. This technical solution effectively eliminates the temperature distortion caused by thermal diffusion, makes the obtained temperature distribution data more in line with the true thermal response of the material, thereby providing a reliable data basis for subsequent thermal insulation performance analysis, and ensuring that the evaluation of the thermal insulation performance of the heat-insulating sponge under complex environmental conditions is more accurate and scientific.

[0130] Referring to Figure 3 , as an embodiment of step S105, based on the temperature distribution matrix and the thermal diffusion coefficient, the steps of calculating the temperature data of different depth layers inside the heat-insulating sponge sample and generating a temperature grid data set include:

[0131] Step S301: Determine the number of depth layers according to the sample thickness of the heat-insulating sponge sample and the preset layer spacing to obtain a depth layer list;

[0132] Among them, in order to calculate the temperatures of different depth layers inside the sample, it is first necessary to calculate the distribution of the depth layers. The sample thickness represents the total depth of the material, and the layer spacing is preset to improve the accuracy of the temperature distribution. The layer spacing is the interval distance between each layer. According to the sample thickness and the preset layer spacing, the required number of layers can be calculated, and these depth layers are arranged in order to form a depth layer list.

[0133] Specifically, the calculation formula for the number of depth layers is: number of layers = sample thickness / layer spacing; assuming that the thickness of the heat-insulating sponge sample is 10 mm and the layer spacing is 1 mm, the obtained depth layer list is [0, 1, 2,..., 10] mm.

[0134] Step S302, based on the temperature distribution matrix and the thermal diffusivity, calculate the temperature of the first layer in the depth layer list according to the heat transfer equation;

[0135] Among them, the first layer in the depth layer list is adjacent to the sample surface. In one embodiment of the present application, based on the temperature distribution matrix (surface temperature data) and the thermal diffusivity, the temperature of the first layer (1 mm depth from the surface) is calculated. The thermal diffusivity (α) is used to describe the rate of heat conduction in the material, and the temperature change can be calculated layer by layer through the heat transfer equation.

[0136] Specifically, the heat transfer equation can be expressed as:

[0137] (6);

[0138] Among them, T n is the temperature of the current depth layer, T n-1 is the temperature of the previous depth layer, represents the temperature difference, Δz is the preset layer spacing, and α is the thermal diffusivity of the material.

[0139] Based on the above formula, the calculation formula for the temperature of the first layer is:

[0140] (7);

[0141] Among them, T 表面 is the surface temperature in the temperature distribution matrix, represents the temperature change between the surface and the first layer.

[0142] Step S303, based on the temperature of the first layer in the depth layer list, calculate the temperature of each layer in the depth layer list in sequence according to the heat transfer formula to obtain the temperature data of all depth layers;

[0143] Among them, the temperature is calculated layer by layer from the first layer to the deepest layer. The temperature calculation for each layer is based on the temperature of the previous layer and the thermal diffusion parameter. The heat transfer formula is gradually applied to each layer to form the temperature distribution from the surface to the deep layer. This layer-by-layer calculation method ensures that the temperature of each depth layer can accurately reflect the heat conduction process of the material.

[0144] Step S304: Arrange the temperature data of all depth layers according to the depth layer list to construct a temperature grid data set.

[0145] Among them, arrange the temperature data of all depth layers in the order of depth levels to construct a complete temperature grid data set. The temperature grid data set provides a structured data model for the temperature distribution from the surface to the deep layer inside the sample, reflects the temperature gradient and heat conduction characteristics of the sample, and provides data support for the multi-level analysis and evaluation of the heat insulation performance.

[0146] In the above embodiments, the temperature data of different depth layers inside the sample are calculated layer by layer based on the temperature distribution matrix and the thermal diffusion coefficient, and finally a temperature grid data set is generated. The temperature grid data set shows the temperature gradient inside the heat insulation sponge sample in a quantitative form, truly reflects the heat insulation effect of the material, and provides a scientific basis for the subsequent performance evaluation and optimization design. This data set not only supports the multi-level analysis of the heat insulation effect, but can also be used as an experimental benchmark to provide a reference for the durability evaluation of heat insulation materials in various application scenarios.

[0147] Refer to Figure 4 , as an embodiment of step S106, the steps of receiving the temperature change data obtained from the high and low temperature cycling test of the heat insulation sponge sample and combining the initial temperature distribution in the temperature grid data set to obtain the high and low temperature heat insulation attenuation curve include:

[0148] Step S401: Receive the temperature change data obtained from the high and low temperature periodic cycling test of the heat insulation sponge sample;

[0149] Among them, in the high and low temperature cycling test, the heat insulation sponge sample will be affected by the changes of different temperature cycles, resulting in the change of the temperature inside the sample with the fluctuation of the high and low temperature cycle. The temperature data during the test can capture the response of the material at different temperature stages.

[0150] It should be noted that this step provides complete temperature change data for the subsequent analysis, especially the temperature distribution of the material in different temperature cycling stages (such as the high temperature stage and the low temperature stage). These data reflect the reaction rate of the material to temperature fluctuations and the change trend of the internal temperature distribution.

[0151] Exemplarily, assume that during the high and low temperature cycle, the temperature on the surface of the sample is 40°C in the high temperature stage and 20°C in the low temperature stage, and the temperature data of different depth layers inside also changes accordingly. The temperature changes at each depth are recorded as [40°C, 35°C, 30°C] to [20°C, 18°C, 15°C].

[0152] Step S402: Based on the initial temperature distribution in the temperature grid dataset, normalize the temperature change data obtained from each temperature cycle test;

[0153] Among them, in order to facilitate the analysis of the test data, compare the temperature change data obtained from each cycle test with the temperature data in the initial temperature distribution, and standardize the temperature change data obtained from the test based on the temperature distribution in the initial temperature grid dataset through normalization, so as to eliminate the interference of different initial temperatures on the data and ensure that the temperature change data of different cycles can be compared consistently.

[0154] Exemplarily, assume that the initial temperature grid dataset is [30°C, 28°C, 26°C], and the temperature change data of a certain test in the high temperature cycle stage is [40°C, 36°C, 33°C], then the normalized temperature change data is [10°C, 8°C, 7°C], which shows the rising amplitude of the temperature in this cycle.

[0155] Step S403: Arrange the normalized temperature change data according to the depth layer list to obtain the temperature change matrix of different depth layers;

[0156] Specifically, organize and arrange the normalized temperature change data according to different depth levels of the material (such as 1mm, 2mm, etc.) to form a temperature change matrix, so as to systematically represent the temperature response changes of the sample in each layer. Among them, the temperature change matrix of different depth layers intuitively shows the temperature distribution differences of the sample in the entire depth layer, providing hierarchical temperature data for the subsequent calculation of the attenuation rate.

[0157] Step S404: Calculate the temperature attenuation rate of each depth layer based on the temperature change matrix;

[0158] Among them, the temperature attenuation rate represents the degree of weakening of the temperature change of the material after multiple high and low temperature cycles. According to the temperature change matrix obtained from each cycle, calculate the attenuation rate of the temperature change of each depth layer to quantify the heat insulation attenuation of the material during the high and low temperature cycle.

[0159] Specifically, the calculation formula is: , by calculating the attenuation rate, it is possible to systematically understand whether the heat insulation performance of the material in different depth layers decays with the number of cycles, which is crucial for evaluating the heat insulation stability of the material during long-term use.

[0160] For example, assume that the normalized temperature change data for the first cycle is [10°C, 8°C, 7°C], and the normalized temperature change data for the second cycle is [9.5°C, 7.6°C, 6.5°C]. Then the temperature decay rates for each depth layer are 0.5°C, 0.4°C, and 0.5°C respectively, indicating that the temperature change of the material gradually weakens.

[0161] Step S405: Arrange the temperature decay rates of each depth layer in each temperature cycle test in chronological order to obtain the high and low temperature heat insulation decay curve.

[0162] Among them, by arranging the temperature decay rates of each cycle test in chronological order and plotting them into a curve, a high and low temperature heat insulation decay curve is generated. Among them, the horizontal axis of the curve represents time or the number of cycles, and the vertical axis represents the temperature decay rates of each depth layer, thereby reflecting the decay trend of the heat insulation performance of the sample as the number of cycles increases.

[0163] It can be understood that in long-term tests, a stable decay curve indicates that the heat insulation performance of the material is stable, and a sharp decline in the curve indicates that the heat insulation performance of the material decays rapidly over time. For example, assume that the high and low temperature heat insulation decay curve of the heat insulation sponge sample shows a gentle downward trend, then it indicates that the material has good durability in heat insulation performance.

[0164] In the above embodiments, starting from the high and low temperature cycle test data, through layers of processing such as normalization, depth level arrangement, and decay rate calculation, the decay curve of the heat insulation material is finally formed, quantifying the heat insulation performance of the material under different depth layers and multiple temperature cycle conditions. This technical solution can not only comprehensively reveal the long-term heat insulation ability of the heat insulation material, but also provide clear data on the heat insulation decay trend, facilitating the evaluation of the stability of the material under long-term extreme conditions, and providing reliable data support and scientific basis for the selection, optimization, and application of the heat insulation material.

[0165] Refer to Figure 5 , as an embodiment of step S107, the steps of receiving the temperature change data and humidity change data obtained from the humidity-temperature cycle test of the heat insulation sponge sample and comparing them with the high and low temperature heat insulation decay curve, and obtaining the humidity-temperature heat insulation decay data set according to the comparison result include:

[0166] Step S501: Receive the temperature change data and humidity change data obtained from the humidity-temperature periodic cycle test of the heat insulation sponge sample;

[0167] Among them, in the humidity-temperature cyclic test, the heat-insulating sponge sample is exposed to an environment with alternating humidity and temperature. The temperature of each internal layer will change accordingly, and at the same time, the influence of humidity on the heat-insulating performance of the material is recorded. This step provides the original data of the internal temperature response of the material under humidity-temperature cycle conditions, as well as the change in the temperature distribution inside the sample in a changing humidity environment, laying a data foundation for subsequent analysis of the influence of humidity on the heat-insulating performance.

[0168] Exemplarily, in the humidity-temperature cycle test, the surface temperature of the sample is 40°C and the humidity is 80% in the high-temperature and high-humidity stage, and the surface temperature of the sample is 20°C and the humidity is 40% in the low-temperature and low-humidity stage. The recorded temperature changes at different depth layers are [40°C, 36°C, 33°C] to [20°C, 18°C, 15°C].

[0169] Step S502, perform normalization processing on the temperature change data and humidity change data;

[0170] Among them, in order to uniformly compare the data, the temperature and humidity change data need to be normalized based on the initial state (initial humidity and initial temperature); normalization can remove the influence of the initial temperature and humidity differences on the data, making the data in different cycle stages comparable.

[0171] Exemplarily, assume that the initial temperature of the heat-insulating sponge sample is 30°C and the humidity is 50% under the humidity-temperature cycle test. When the temperature rises to 40°C and the humidity rises to 80%, the normalized temperature change is 10°C and the humidity change is 30%.

[0172] Step S503, arrange the normalized temperature change data and humidity change data according to the depth layer list to obtain a humidity-temperature change matrix for different depth layers;

[0173] Among them, the normalized temperature change data and humidity change data are arranged according to the depth levels of the sample to generate a humidity-temperature change matrix to show the temperature response of different depth layers in a changing humidity environment, providing data support for calculating the temperature attenuation rate layer by layer.

[0174] Step S504, based on the humidity-temperature change matrix, calculate the temperature attenuation rate of each depth layer to generate a corresponding temperature attenuation rate matrix;

[0175] Specifically, the calculation formula is: , by calculating the temperature data in the humidity-temperature heat-insulating performance change matrix layer by layer, the temperature attenuation rate matrix under the humidity-temperature cycle test is obtained. Among them, the temperature attenuation rate matrix reveals the heat-insulating performance attenuation of the material at different depths under humidity-temperature cycle conditions, providing a basis for comparison with the high and low temperature attenuation rates later.

[0176] Step S505: Layer by layer, compare the temperature decay rate in the temperature decay rate matrix with the temperature decay rate in the high and low temperature heat insulation decay curve to obtain a comparison result;

[0177] Among them, layer by layer compare the humidity-temperature decay rate with the temperature decay rate of the high and low temperature decay curve to judge the specific influence of humidity on heat insulation decay. Through the layer-by-layer comparison method, it is possible to refine the analysis and judge the enhancement or weakening effect of humidity on the heat insulation performance of different depth layers. For example, if the decay rates of each depth layer in the high and low temperature decay rate matrix are [2%, 1.5%, 1%], and the decay rates of each layer in the humidity-temperature decay rate matrix are [3%, 2.5%, 2%], then the heat insulation decay rate of each layer under the humidity environment increases by 1%, 0.5% and 1% respectively, indicating that the humidity condition exacerbates the heat insulation decay.

[0178] Step S506: Based on the comparison result, establish a humidity-temperature heat insulation decay data set.

[0179] Among them, summarize the layer-by-layer comparison results to generate a humidity-temperature heat insulation decay data set. The data set quantifies the influence of humidity change on the heat insulation performance of the material, providing reliable data support for the performance analysis of the material under humid conditions.

[0180] Exemplarily, assume that the comparison result shows that when the humidity is 80%, the temperature decay rate of the sample increases by 5%. Then the data set records the weakening situation of the humidity increase on the heat insulation performance of the sample, providing quantitative analysis support for the application of the material in a humidity-sensitive environment.

[0181] In the above embodiments, a quantitative evaluation of the temperature decay characteristics of the heat insulation sponge sample under the humidity-temperature cycling environment is realized. The finally generated humidity-temperature heat insulation decay data set can effectively reveal the degree of weakening of the humidity change on the heat insulation performance. Especially in a humid environment, the heat insulation persistence and stability of the material are scientifically quantified and evaluated, providing accurate decision-making support for the applicability of the material in a specific environment.

[0182] Refer to Figure 6 , as an embodiment of step S108, the steps of analyzing the heat insulation decay trend according to the high and low temperature heat insulation decay curve and the humidity-temperature heat insulation decay data set to obtain the heat insulation performance data of the heat insulation sponge sample include:

[0183] Step S601: Normalize the high and low temperature heat insulation decay curve and the humidity-temperature heat insulation decay data set;

[0184] Among them, since the high and low temperature heat insulation attenuation curve and the humidity-temperature heat insulation attenuation data set respectively represent the attenuation of heat insulation performance under different environmental conditions, it is necessary to normalize the data to unify the reference value and ensure that the two data can be effectively compared on the same scale.

[0185] Exemplarily, assume that the temperature attenuation rates in the high and low temperature attenuation curve are [0.5%, 0.4%, 0.3%], and the temperature attenuation rates in the humidity-temperature attenuation data set are [0.7%, 0.6%, 0.5%]. The two sets of data are adjusted to the same reference through normalization, for example, normalized into the form of relative attenuation rates.

[0186] Step S602: Layer-by-layer compare the normalized high and low temperature heat insulation attenuation curve and the humidity-temperature heat insulation attenuation data set to obtain a difference matrix.

[0187] Among them, the difference matrix shows the additional attenuation amount under humidity change conditions compared with single temperature change conditions, reflects the additional influence of humidity on heat insulation performance, can quantify the influence of humidity on heat insulation performance at different temperatures, and provides quantitative difference data for subsequent curve fitting.

[0188] Exemplarily, assume that the high and low temperature attenuation rate matrix is [0.5%, 0.4%, 0.3%], and the humidity-temperature attenuation rate matrix is [0.7%, 0.6%, 0.5%]. Then the difference matrix is [0.2%, 0.2%, 0.2%], indicating that the humidity condition increases the attenuation by an additional 0.2%.

[0189] Step S603: Based on the difference matrix, calculate the heat insulation attenuation trend curve of the heat insulation sponge sample through a trend fitting algorithm, and fit to obtain a heat insulation attenuation trend model.

[0190] Specifically, use the data in the difference matrix for trend fitting analysis (such as linear regression, exponential decay model, etc.) to obtain a heat insulation attenuation trend curve. The selection of the fitting algorithm needs to consider the data characteristics and attenuation behavior, such as whether it shows linear attenuation or exponential attenuation. The fitted heat insulation attenuation trend model can describe the attenuation trend of the sample under high and low temperature and humidity conditions through a mathematical formula, and provide a visual curve of the heat insulation attenuation behavior under different environments.

[0191] As one embodiment of the present application, in the fitting analysis process, if the attenuation trend of the difference matrix data conforms to the exponential model, the curve form of the fitted attenuation model is:

[0192] (8);

[0193] Among them, the parameters a and b respectively represent the initial attenuation rate and the change coefficient of the attenuation rate.

[0194] Step S604: Based on the heat insulation attenuation trend model, calculate the comprehensive heat insulation attenuation rates under high and low temperature conditions and humidity-temperature conditions;

[0195] Among them, the comprehensive heat insulation attenuation rate under long-term high and low temperature cycles and humidity-temperature conditions is calculated using the heat insulation attenuation trend model. The comprehensive heat insulation attenuation rate reflects the average heat insulation performance attenuation rate of the sample under multiple environmental conditions and is a key indicator of the long-term use stability of the material.

[0196] Exemplarily, if the annual attenuation rate under high and low temperature conditions is 0.5% and the annual attenuation rate under humidity-temperature conditions is 0.8%, then the comprehensive attenuation rate is 0.65% / year.

[0197] Step S605: Obtain the heat insulation performance data of the heat insulation sponge sample according to the heat insulation attenuation trend model and the comprehensive heat insulation attenuation rate.

[0198] Among them, according to the obtained heat insulation attenuation trend model and comprehensive heat insulation attenuation rate, summarize and generate the heat insulation performance data of the sample. The heat insulation performance data includes the heat insulation attenuation rates under different environmental factors and fully reflects the long-term heat insulation characteristics of the material for evaluating the heat insulation application performance of the material.

[0199] In the above embodiments, a comprehensive analysis of the heat insulation attenuation trend under multiple environmental factors is achieved. Through normalization, calculation of high and low temperature and humidity differences, attenuation trend fitting, and layer-by-layer calculation of the comprehensive attenuation rate, the heat insulation stability of the heat insulation material during long-term use is quantitatively evaluated. The finally generated heat insulation performance data can provide a scientific basis for the long-term applicability of the heat insulation material and support the optimized selection and design of the material under composite environmental conditions such as high temperature and humidity.

[0200] Refer to Figure 7 , as an embodiment of step S107, after the step of obtaining the humidity-temperature heat insulation attenuation data set according to the comparison result, the following steps are further included:

[0201] Step S701: Input the high and low temperature heat insulation attenuation curve and the humidity-temperature heat insulation attenuation data set into a pre-constructed finite element model to obtain a finite element microstructure model;

[0202] Among them, the finite element model is a numerical simulation technology. Through discretization, the heat transfer, attenuation process, etc. of the material can be decomposed into grid-like calculation units. The temperature changes and heat conduction rates of each grid unit can be determined by the input data, and thus the three-dimensional heat conduction characteristics of the sponge can be constructed.

[0203] In the embodiments of the present application, the high-low temperature heat insulation attenuation curve and the humidity-temperature heat insulation attenuation data set can reflect the heat insulation performance of the heat insulation sponge sample under different environmental conditions. By inputting these data into the finite element model, simulating the heat conduction path and attenuation rate of the pore distribution inside the sample, a microstructure model can be constructed, and thus the internal structure and pore distribution characteristics of the heat insulation sponge can be further analyzed based on the actual attenuation data.

[0204] It can be understood that the finite element microstructure model can further simulate the internal heat transfer path and conduction characteristics of the sponge in the simulation model, provide a visual and data-based model of the heat conduction of the sample in the actual environment, and provide a real basis for subsequent parameter optimization.

[0205] Step S702: Adjust the internal parameters of the finite element microstructure model based on the temperature grid data set to obtain a microstructure simulation model of the heat insulation sponge sample;

[0206] Among them, the temperature grid data set contains the temperature distribution at different depths of the sample. By comparing the temperature information in the temperature grid data set and adjusting the internal parameters (such as the heat conduction coefficient and the heat diffusion coefficient, etc.) of the finite element microstructure model, the simulation result of the model can be made closer to the actual temperature distribution. This adjustment can refine the simulation model and make it more in line with the actual heat insulation performance of the sample.

[0207] In the embodiments of the present application, temperature parameters can be set in different depth layers of the model, observe the performance of the heat conduction path in the simulation model, compare it layer by layer with the temperature grid data set, and make fine adjustments to achieve consistency. For example, set the surface grid temperature to 32 °C, the next layer depth to 31 °C, and adjust the temperature distribution of the model in turn to ensure consistency with the actual temperature grid, so as to generate a microstructure simulation model that more conforms to the actual heat conduction characteristics of the sample, laying an accurate model foundation for subsequent parameter optimization and performance analysis.

[0208] Specifically, the model structure of the above-mentioned microstructure simulation model specifically includes:

[0209] 1. Mesh division layer: Discretize the three-dimensional structure of the heat insulation sponge sample into small mesh units. Each mesh unit represents a microscopic volume of the material, which is composed of the pore structure and the solid matrix of the material; through mesh division, the heat conduction process of the material is discretized, which is convenient for calculating the heat transfer and temperature change of each small unit, and provides a detailed heat conduction analysis.

[0210] 2. Stomata and matrix distribution layer: Within each grid cell, further subdivide the ratio and spatial distribution of stomata and solid matrix, set different combinations of stomatal density and pore size. The distribution of stomata directly affects the heat conduction path, and the matrix part is responsible for heat conduction. Among them, stomatal density and pore size control the heat insulation performance of the material. The higher the density and the smaller the pore size, the better the heat insulation effect usually is. By adjusting these parameters, the simulation model can observe the influence of different microstructures on heat conduction.

[0211] 3. Heat conduction path layer: The thermal conductivity and thermal diffusivity of each grid cell are determined by the finite element calculation module. The heat conduction path layer is responsible for calculating the heat transfer path between stomata and matrix, specifically considering the distribution of stomata and the thermal conductivity of the matrix. This layer defines how heat is transferred inside the sample, and can effectively simulate the layer-by-layer diffusion of heat flow in the sponge structure, helping to predict the heat insulation effect of the material.

[0212] 4. Temperature distribution layer: This layer records the temperature distribution of different depth layers, calibrates the temperature field distribution based on the input temperature grid data set, so that the temperature distribution of the simulation model is consistent with the actual temperature change. The temperature distribution layer improves the accuracy of the model by adjusting the difference between the simulation result and the actual temperature data, to ensure that the simulation result matches the real situation.

[0213] 5. Heat flow field and boundary condition layer: Used to set the heat flow boundary conditions, including the heat source position, external environment temperature, and heat dissipation conditions on the sample surface. The heat flow field describes the process of heat energy transfer from the high-temperature source to the low-temperature region. The boundary condition layer can realistically simulate the heating situation of the heat insulation material in the actual environment, and predict the heat insulation performance of the material under high temperature or humidity conditions by simulating the heat flow changes in different environments.

[0214] 6. Parameter adjustment and performance optimization layer: This layer allows adjusting parameters such as stomatal density and pore size in the simulation model, observing the changes in the heat insulation performance of the material under different microstructural combinations, and calculating the heat insulation performance indicators such as thermal conductivity and thermal diffusivity for each parameter combination. By adjusting the ratio of stomata and matrix, identify the optimal structural combination, and help optimize the material design to make it achieve the best heat insulation performance.

[0215] Based on this, the microstructure simulation model of the embodiment of the present application comprehensively simulates the heat conduction characteristics of the heat insulation sponge sample through the structural construction of grid division, stomata and matrix distribution, heat conduction path, temperature distribution, heat flow field and boundary conditions, and parameter adjustment levels. This model can not only accurately display the heat conduction path of the material in a complex environment, but also adjust the structural parameters to achieve performance optimization, thereby providing reliable data support for improving the heat insulation performance of the material.

[0216] Step S703: Adjust the pore density and pore size parameters in the microstructure simulation model, calculate the heat insulation performance data of the model according to each group of parameter combinations, and generate an optimized data set of heat insulation performance;

[0217] Among them, the pore density and pore size directly affect the heat insulation effect of the heat insulation sponge. By adjusting these parameters in the simulation model, the heat insulation performance of the material under different pore structure combinations can be observed, and its heat insulation performance data can be calculated. Integrate the performance data of these different parameter combinations to form an optimized data set of heat insulation performance for subsequent analysis of the optimal combination.

[0218] Exemplarily, in the microstructure simulation model, gradually adjust the pore density (such as 10%, 20%, 30%) and pore size (such as 100 microns, 200 microns, 300 microns), simulate the heat insulation performance of each group of parameter combinations, record the heat insulation data such as the thermal conductivity and thermal diffusivity of each combination, and form an optimized data set of heat insulation performance, which helps to comprehensively analyze the influence of different pore densities and pore sizes on the heat insulation performance and provides data support for selecting the optimal parameter combination.

[0219] Step S704: Based on the optimized data set of heat insulation performance, select the parameter combination with the best heat insulation performance data and generate structural optimization suggestion information for the heat insulation sponge sample.

[0220] Among them, by analyzing different parameter combinations in the optimized data set of heat insulation performance, select the parameter combination with the best heat insulation performance, and correspondingly generate structural optimization suggestion information, thus providing quantifiable improvement suggestions for material design and manufacturing to ensure that the heat insulation sponge sample has the best performance in application.

[0221] Exemplarily, screen out the pore density and pore size combination with the highest thermal resistance and the lowest thermal conductivity from the optimized data set of heat insulation performance. For example, the combination with a pore density of 25% and a pore size of 150 microns. Generate optimization suggestion information according to this optimal parameter combination, suggesting that the structural parameters of the material be set to the above optimal combination to achieve the best heat insulation performance and make the material have a more stable and lasting heat insulation effect in the actual use environment.

[0222] In the above embodiments, the heat insulation performance evaluation of the heat insulation sponge material under complex environmental conditions is realized. Through multi-step processing and simulation, not only can the attenuation trend of the heat insulation material be quantified, but also the relationship between the internal structure of the material and the heat insulation performance can be deeply analyzed by using the microstructure simulation model. Finally, scientific suggestions for optimizing the material structure are provided. This technical solution significantly improves the accuracy of the heat insulation durability evaluation of the heat insulation sponge material and provides comprehensive data support and optimization guidance for the design, selection and application of the heat insulation material.

[0223] The present application provides a quantitative performance analysis method for thermal insulation materials in complex environments, which can not only support the optimized design of materials, but also be applied in fields such as construction and industry to ensure the stable thermal insulation performance of materials in different environments. Through a scientific evaluation system and data guidance, it provides a reliable basis for material selection and research and development, promoting the efficient application and long-term performance improvement of thermal insulation materials

[0224] An embodiment of the present application also discloses a thermal insulation performance evaluation system for a thermal insulation sponge.

[0225] A thermal insulation performance evaluation system for a thermal insulation sponge includes:

[0226] A parameter acquisition module for acquiring the initial parameters of a thermal insulation sponge sample to be detected; wherein, the initial parameters include the sample thickness, sample surface area, pore density, and pore size of the thermal insulation sponge sample;

[0227] A first data processing module for receiving and storing the heat transfer amount data of the thermal insulation sponge sample under different temperature difference conditions to obtain a temperature-heat transfer amount curve of the thermal insulation sponge sample;

[0228] A second data processing module for generating a temperature-thermal conductivity curve of the thermal insulation sponge sample based on the temperature-heat transfer amount curve and the sample thickness through the steady-state heat flow formula, and calculating the thermal diffusivity data;

[0229] A temperature distribution matrix generation module for receiving the thermal map data of the surface temperature distribution of the thermal insulation sponge sample and calibrating the temperature response based on the thermal diffusivity to generate a temperature distribution matrix;

[0230] A temperature grid data set generation module for calculating the temperature data of different depth layers inside the thermal insulation sponge sample based on the temperature distribution matrix and the thermal diffusivity to generate a temperature grid data set;

[0231] A third data processing module for receiving the temperature change data obtained from the high and low temperature cycle test of the thermal insulation sponge sample, and combining with the initial temperature distribution in the temperature grid data set to obtain a high and low temperature thermal insulation attenuation curve;

[0232] A fourth data processing module for receiving the temperature change data and humidity change data obtained from the humidity-temperature cycle test of the thermal insulation sponge sample, and comparing with the high and low temperature thermal insulation attenuation curve to obtain a humidity-temperature thermal insulation attenuation data set according to the comparison result;

[0233] A thermal insulation performance data generation module for performing thermal insulation attenuation trend analysis based on the high and low temperature thermal insulation attenuation curve and the humidity-temperature thermal insulation attenuation data set to obtain the thermal insulation performance data of the thermal insulation sponge sample.

[0234] In the above embodiments, through multi-level data collection and processing, the accurate thermal insulation performance evaluation of thermal insulation materials under multiple environmental conditions is realized. By obtaining different environmental attenuation data, analyzing the temperature distribution, and modeling the attenuation trend, the system can generate comprehensive thermal insulation performance data, providing a high-precision evaluation method for the durability and stability of thermal insulation materials.

[0235] The thermal insulation performance evaluation system of a thermal insulation sponge according to an embodiment of the present application can implement any of the above methods for evaluating the thermal insulation performance of a thermal insulation sponge, and the specific working processes of each module in the thermal insulation performance evaluation system of a thermal insulation sponge can refer to the corresponding processes in the above method embodiments.

[0236] In several embodiments provided by the present application, it should be understood that the provided methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for example, the division of a certain module is only a logical function division, and there may be other division methods in actual implementation. For example, multiple modules can be combined or integrated into another system, or some features can be ignored or not executed.

[0237] An embodiment of the present application also discloses a computer device.

[0238] The computer device includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements the method for evaluating the thermal insulation performance of a thermal insulation sponge as described above.

[0239] An embodiment of the present application also discloses a computer-readable storage medium.

[0240] The computer-readable storage medium stores a computer program that can be loaded and executed by a processor to implement any of the methods for evaluating the thermal insulation performance of a thermal insulation sponge as described above.

[0241] Among them, the computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, device, or device; the program code contained on the computer-readable medium can be transmitted by any suitable medium, including but not limited to wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0242] It should be noted that in the above embodiments, the descriptions of each embodiment have their own emphases. For parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0243] The above are all preferred embodiments of the present application, and do not limit the protection scope of the present application accordingly. Any feature disclosed in this specification (including the abstract and drawings), unless specifically described, can be replaced by other equivalent or similar-purpose alternative features. That is, unless specifically described, each feature is only an example of a series of equivalent or similar features.

Claims

1. A method for evaluating the heat insulation performance of a heat-insulating sponge, characterized in that, The performance evaluation method includes: Obtaining the initial parameters of the heat-insulating sponge sample to be detected; wherein, the initial parameters include the sample thickness, sample surface area, pore density, and pore size of the heat-insulating sponge sample; Receiving and storing the heat transfer amount data of the heat-insulating sponge sample under different temperature difference conditions to obtain the temperature-heat transfer amount curve of the heat-insulating sponge sample; Based on the temperature-heat transfer amount curve and the sample thickness, generating the temperature-thermal conductivity curve of the heat-insulating sponge sample through the steady-state heat flow formula and calculating the heat diffusion coefficient data; Receiving the thermal image data of the surface temperature distribution of the heat-insulating sponge sample and calibrating the temperature response based on the heat diffusion coefficient to generate a temperature distribution matrix; Based on the temperature distribution matrix and the heat diffusion coefficient, calculating the temperature data of different depth layers inside the heat-insulating sponge sample to generate a temperature grid data set; Receiving the temperature change data obtained from the high and low temperature cycle test of the heat-insulating sponge sample, and combining with the initial temperature distribution in the temperature grid data set to obtain the high and low temperature heat insulation attenuation curve; Receiving the temperature change data and humidity change data obtained from the humidity-temperature cycle test of the heat-insulating sponge sample, comparing with the high and low temperature heat insulation attenuation curve, and obtaining the humidity-temperature heat insulation attenuation data set according to the comparison result; Performing heat insulation attenuation trend analysis according to the high and low temperature heat insulation attenuation curve and the humidity-temperature heat insulation attenuation data set to obtain the heat insulation performance data of the heat-insulating sponge sample.

2. The method for evaluating the heat insulation performance of a heat insulation sponge according to claim 1, wherein The steps of receiving the thermal image data of the surface temperature distribution of the heat-insulating sponge sample and calibrating the temperature response based on the heat diffusion coefficient to generate a temperature distribution matrix include: Receiving the thermal image data of the surface temperature distribution of the heat-insulating sponge sample; Calculating the temperature response delay time on the surface of the heat-insulating sponge sample based on the heat diffusion coefficient; Adjusting the temperature amplitude in the thermal image data based on the temperature response delay time to calibrate each temperature value in the thermal image data; Arranging the calibrated temperature values according to the original position information of the thermal image data to form a temperature distribution matrix; Quantifying the calibration accuracy of the temperature distribution matrix based on a statistical method and verifying the temperature distribution matrix according to the calibration accuracy.

3. The method for evaluating the heat insulation performance of a heat-insulating sponge according to claim 1, characterized in that, The steps of calculating the temperature data of different depth layers inside the heat-insulating sponge sample based on the temperature distribution matrix and the heat diffusion coefficient to generate a temperature grid data set include: Determining the number of depth layers according to the sample thickness of the heat-insulating sponge sample and the preset layer spacing to obtain a depth layer list; Calculating the temperature of the first layer in the depth layer list based on the temperature distribution matrix and the heat diffusion coefficient according to the heat transfer equation; Based on the temperature of the first layer in the depth layer list, calculating the temperature of each layer in the depth layer list in turn according to the heat transfer formula to obtain the temperature data of all depth layers; Arranging the temperature data of all depth layers according to the depth layer list to construct a temperature grid data set.

4. The method for evaluating the heat insulation performance of a heat-insulating sponge according to claim 3, characterized in that, The steps of receiving the temperature change data obtained from the high and low temperature cycle test of the heat-insulating sponge sample, and combining with the initial temperature distribution in the temperature grid data set to obtain the high and low temperature heat insulation attenuation curve include: Receive the temperature change data obtained from the high and low temperature cyclic test of the heat insulation sponge sample; Based on the initial temperature distribution in the temperature grid dataset, normalize the temperature change data obtained from each temperature cycle test; Arrange the normalized temperature change data according to the depth layer list to obtain a temperature change matrix for different depth layers; Based on the temperature change matrix, calculate the temperature attenuation rate of each depth layer; Arrange the temperature attenuation rates of each depth layer in each temperature cycle test in chronological order to obtain a high and low temperature heat insulation attenuation curve.

5. The method for evaluating the heat insulation performance of a heat insulation sponge according to claim 4, characterized in that, The steps of receiving the temperature change data and humidity change data obtained from the humidity-temperature cycle test of the heat insulation sponge sample, comparing them with the high and low temperature heat insulation attenuation curve, and obtaining a humidity-temperature heat insulation attenuation dataset according to the comparison result include: Receive the temperature change data and humidity change data obtained from the humidity-temperature cyclic test of the heat insulation sponge sample; Normalize the temperature change data and humidity change data; Arrange the normalized temperature change data and humidity change data according to the depth layer list to obtain a humidity-temperature change matrix for different depth layers; Based on the humidity-temperature change matrix, calculate the temperature attenuation rate of each depth layer to generate a corresponding temperature attenuation rate matrix; Compare the temperature attenuation rates in the temperature attenuation rate matrix layer by layer with the temperature attenuation rates in the high and low temperature heat insulation attenuation curve to obtain a comparison result; Based on the comparison result, establish a humidity-temperature heat insulation attenuation dataset.

6. The method for evaluating the heat insulation performance of a heat-insulating sponge according to claim 5, characterized in that, The steps of obtaining the heat insulation performance data of the heat insulation sponge sample by performing heat insulation attenuation trend analysis according to the high and low temperature heat insulation attenuation curve and the humidity-temperature heat insulation attenuation dataset include: Normalize the high and low temperature heat insulation attenuation curve and the humidity-temperature heat insulation attenuation dataset; Compare the normalized high and low temperature heat insulation attenuation curve and the humidity-temperature heat insulation attenuation dataset layer by layer to obtain a difference matrix; Based on the difference matrix, calculate the heat insulation attenuation trend curve of the heat insulation sponge sample through a trend fitting algorithm, and fit to obtain a heat insulation attenuation trend model; Based on the heat insulation attenuation trend model, calculate the comprehensive heat insulation attenuation rates under high and low temperature conditions and humidity-temperature conditions; According to the heat insulation attenuation trend model and the comprehensive heat insulation attenuation rate, obtain the heat insulation performance data of the heat insulation sponge sample.

7. A method for evaluating the heat insulation performance of a heat-insulating sponge according to any one of claims 1 to 6, characterized in that After the step of obtaining a humidity-temperature heat insulation attenuation dataset according to the comparison result, it further includes: Input the high and low temperature heat insulation attenuation curve and the humidity-temperature heat insulation attenuation dataset into a pre-constructed finite element model to obtain a finite element microstructure model; Based on the temperature grid dataset, adjust the internal parameters of the finite element microstructure model to obtain a microstructure simulation model of the heat insulation sponge sample; Adjust the pore density and pore size parameters in the microstructure simulation model, calculate the heat insulation performance data of the model according to each parameter combination, and generate a heat insulation performance optimization data set; Based on the optimized data set of the heat insulation performance, select the parameter combination with the optimal heat insulation performance data, and generate the structural optimization suggestion information of the heat insulation sponge sample.

8. An evaluation system for the heat insulation performance of a heat-insulating sponge, characterized in that The performance evaluation system includes: A parameter acquisition module for acquiring the initial parameters of the heat insulation sponge sample to be detected; wherein, the initial parameters include the sample thickness, sample surface area, pore density and pore size of the heat insulation sponge sample; A first data processing module for receiving and storing the heat transfer amount data of the heat insulation sponge sample under different temperature difference conditions to obtain the temperature-heat transfer amount curve of the heat insulation sponge sample; A second data processing module for generating the temperature-thermal conductivity curve of the heat insulation sponge sample based on the temperature-heat transfer amount curve and the sample thickness through the steady-state heat flow formula, and calculating the thermal diffusivity data; A temperature distribution matrix generation module for receiving the heat map data of the surface temperature distribution of the heat insulation sponge sample and generating a temperature distribution matrix based on the calibration of the temperature response by the thermal diffusivity; A temperature grid data set generation module for calculating the temperature data of different depth layers inside the heat insulation sponge sample based on the temperature distribution matrix and the thermal diffusivity, and generating a temperature grid data set; A third data processing module for receiving the temperature change data obtained from the high and low temperature cycle test of the heat insulation sponge sample, and combining the initial temperature distribution in the temperature grid data set to obtain the high and low temperature heat insulation attenuation curve; A fourth data processing module for receiving the temperature change data and humidity change data obtained from the humidity-temperature cycle test of the heat insulation sponge sample, comparing them with the high and low temperature heat insulation attenuation curve, and obtaining the humidity-temperature heat insulation attenuation data set according to the comparison result; A heat insulation performance data generation module for performing heat insulation attenuation trend analysis according to the high and low temperature heat insulation attenuation curve and the humidity-temperature heat insulation attenuation data set to obtain the heat insulation performance data of the heat insulation sponge sample.

9. A computer device, characterized in that: It includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: Stores a computer program that can be loaded and executed by a processor to implement the method according to any one of claims 1 to 7.

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