Method for detecting thermal insulation performance of low-carbon building material

Abstract

The invention provides a thermal insulation performance detection method of a low-carbon building material, and relates to the technical field of thermal insulation performance detection, a performance simulation detection system is constructed, temperature control is carried out, a temperature characteristic infrared image is obtained, and a temperature image change sequence is further obtained; calculating an adjacent feature change coefficient, and obtaining target temperature image data; acquiring heat balance image data according to the adjacent temperature image data, and further acquiring heat balance temperature difference data; the same feature data and different feature data of a plurality of layers are obtained through heat balance image data corresponding to the heat balance temperature difference data, then a same feature data sequence and a different feature data sequence are obtained, the heat insulation performance coefficient is calculated, the heat insulation performance state is judged, and performance state judgment information is obtained. According to the invention, the combination of simulated thermal insulation detection and infrared image analysis can be realized, and the precision and efficiency of performance detection are improved.

Description

Technical Field

[0001] The present invention provides a method for detecting the thermal insulation performance of low-carbon building materials, and relates to the technical field of performance detection, in particular to the technical field of thermal insulation performance detection. Background Art

[0002] Low-carbon building materials are widely used in the construction field. Their thermal insulation performance directly affects the energy consumption and indoor environmental comfort of buildings. Therefore, it is very important to accurately test the thermal insulation performance of low-carbon building materials.

[0003] Traditional low-carbon building material performance testing methods usually only use dynamic cold and hot monitoring methods or only use infrared detection methods, which makes it impossible to take into account both simulation testing and precise image analysis testing. As a result, traditional testing methods are difficult to meet the requirements of detection accuracy and detection efficiency at the same time. At the same time, traditional testing methods are easily affected by temperature differences when the temperature difference between the two sides is large, resulting in low accuracy of the test results. Summary of the Invention

[0004] The present invention provides a method for testing the thermal insulation performance of low-carbon building materials to solve the above problems:

[0005] The present invention provides a method for testing the thermal insulation performance of low-carbon building materials, the method comprising:

[0006] S1. Build a performance simulation detection system, perform temperature control on the performance simulation detection system, obtain temperature difference data at each time series node, and then obtain temperature characteristic infrared images under temperature control change information at different time series nodes;

[0007] S2. Obtaining a temperature image change sequence based on the temperature difference data;

[0008] S3, obtaining adjacent feature variation coefficients of adjacent temperature image data according to the temperature image variation sequence, and obtaining target temperature image data according to the adjacent feature variation coefficients;

[0009] S4. Acquire thermal balance image data based on adjacent temperature image data, and then acquire thermal balance temperature difference data;

[0010] S5. Obtain multiple levels of identical feature data and different feature data through the thermal balance image data corresponding to the thermal balance temperature difference data, and then obtain identical feature data sequences and different feature data sequences, calculate the thermal insulation performance coefficient, perform thermal insulation performance status determination, and obtain performance status determination information.

[0011] Furthermore, the system includes:

[0012] The simulation temperature control detection module is used to build a performance simulation detection system, perform temperature control on the performance simulation detection system, obtain temperature difference data at each time series node, and then obtain temperature characteristic infrared images under temperature control change information at different time series nodes;

[0013] A change sequence acquisition module is used to acquire a temperature image change sequence based on temperature difference data;

[0014] A stable change target acquisition module is used to obtain adjacent feature change coefficients of adjacent temperature image data according to the temperature image change sequence, and obtain target temperature image data according to the adjacent feature change coefficients;

[0015] A thermal balance temperature difference acquisition module is used to acquire thermal balance image data based on adjacent temperature image data, and further acquire thermal balance temperature difference data;

[0016] The performance analysis module is used to obtain multiple levels of identical feature data and different feature data through the thermal balance image data corresponding to the thermal balance temperature difference data, and then obtain identical feature data sequences and different feature data sequences, calculate the thermal insulation performance coefficient, perform thermal insulation performance status judgment, and obtain performance status judgment information.

[0017] The present invention has the following beneficial effects: by constructing a simulation detection system and acquiring data with multiple time series and multiple temperature differences, it is possible to fully simulate various temperature environments in which materials are actually used, accurately evaluate the thermal insulation performance of materials under different temperature differences, and avoid the limitations brought about by a single test condition, preventing the temperature difference on both sides from affecting the material, resulting in inaccurate collected infrared images, and thus inaccurate analysis results;

[0018] The analysis of the temperature image change sequence and the adjacent feature change coefficients can dynamically reflect the detailed process of the temperature difference change on the material surface, provide an in-depth understanding of the material's performance at different temperature stages, and discover potential performance problems or advantages.

[0019] Screening target temperature image data and extracting feature data at different levels can accurately locate the normal and abnormal characteristics of the material's thermal insulation performance, enhancing the accuracy and efficiency of material performance analysis.

[0020] The thermal insulation performance coefficient is calculated based on the comprehensive characteristic data and the performance status is determined, which further makes the determination of the material performance status more accurate and efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 Schematic diagram of a method for testing the thermal insulation performance of a low-carbon building material. DETAILED DESCRIPTION

[0022] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0023] One embodiment of the present invention provides a method for testing the thermal insulation performance of low-carbon building materials, the method comprising:

[0024] S1. Build a performance simulation detection system, perform temperature control on the performance simulation detection system, obtain temperature difference data at each time series node, and then obtain temperature characteristic infrared images under temperature control change information at different time series nodes;

[0025] S2. Obtaining a temperature image change sequence based on the temperature difference data;

[0026] S3, obtaining adjacent feature variation coefficients of adjacent temperature image data according to the temperature image variation sequence, and obtaining target temperature image data according to the adjacent feature variation coefficients;

[0027] S4. Acquire thermal balance image data based on adjacent temperature image data, and then acquire thermal balance temperature difference data;

[0028] S5. Obtain the same feature data and different feature data at multiple levels through the thermal balance image data corresponding to the thermal balance temperature difference data, and then obtain the same feature data sequence and the different feature data sequence, calculate the thermal insulation performance coefficient, perform thermal insulation performance status determination, and obtain performance status determination information, such as Figure 1 shown.

[0029] The working principle and technical effect of the above technical solution are as follows: a performance simulation detection system is built, and the system's temperature is precisely controlled to simulate the temperature environment under different actual working conditions. During system operation, the temperature difference data of each time series node is recorded, and infrared imaging technology is used to obtain infrared images of the temperature characteristics corresponding to the temperature control changes. The images intuitively show the surface temperature distribution of the material under different temperature conditions.

[0030] Based on the acquired temperature difference data, the temperature characteristic infrared images at different time nodes are arranged according to the size of the temperature difference to form a temperature image change sequence, which reflects the dynamic process of the material surface temperature changing with the temperature difference.

[0031] Adjacent temperature image data in a temperature image change sequence is analyzed to calculate adjacent feature variation coefficients. These coefficients quantify the characteristic differences between adjacent images, such as the degree of change in temperature distribution. By analyzing these coefficients, target temperature image data that remains stable after a temperature change (temperature changes can lead to significant temperature differences between the two sides of the material, resulting in image instability, so the image is stable). This data can highlight important features of the material during its stable state.

[0032] Based on the adjacent temperature image data, the image data when the material reaches thermal equilibrium is further analyzed, i.e., the thermal equilibrium image data. At the same time, the corresponding thermal equilibrium temperature difference data is obtained. This data reflects the temperature difference between the two sides of the material in the thermal equilibrium state and is an important reference for evaluating the thermal insulation performance of the material. The thermal equilibrium state here means that although there is a temperature difference between the two sides of the material, the influence of the temperature on the internal temperature of the material has stabilized. At this time, the image of the material is relatively accurate.

[0033] From the thermal balance image data corresponding to the thermal balance temperature difference data, multiple levels of identical feature data (and different feature data) are extracted to form identical feature data sequences and different feature data sequences, respectively. By comprehensively analyzing these feature data sequences, the thermal insulation performance coefficient is calculated. Finally, the thermal insulation performance status is determined based on the coefficient value to obtain performance status determination information.

[0034] By building a simulation detection system and acquiring data with multiple time series and multiple temperature differences, we can fully simulate the various temperature environments in which the material will be used in actual use, accurately evaluate the thermal insulation performance of the material under different temperature differences, and avoid the limitations brought by a single test condition, preventing the temperature difference on both sides from affecting the material, resulting in inaccurate infrared images and, in turn, inaccurate analysis results.

[0035] The analysis of the temperature image change sequence and the adjacent feature change coefficients can dynamically reflect the detailed process of the temperature difference change on the material surface, provide an in-depth understanding of the material's performance at different temperature stages, and discover potential performance problems or advantages.

[0036] Screening target temperature image data and extracting feature data at different levels can accurately locate the normal and abnormal characteristics of the material's thermal insulation performance, enhancing the accuracy and efficiency of material performance analysis.

[0037] The thermal insulation performance coefficient is calculated based on the comprehensive characteristic data and the performance status is determined, which further makes the determination of the material performance status more accurate and efficient.

[0038] In one embodiment of the present invention, S1 includes:

[0039] A thermal simulation environment is constructed by a heater to obtain information about the hot box and the thermal simulation environment;

[0040] A cold simulation environment is constructed by a refrigerator to obtain cold box and cold simulation environment information;

[0041] Placing the low-carbon building material at the connection point of a hot box and a cold box to obtain a performance simulation detection system;

[0042] Perform temperature control of the hot box and cold box of the performance simulation detection system to obtain temperature control information of the performance simulation detection system;

[0043] Infrared thermal box images of low-carbon building materials are collected according to the temperature control information to obtain thermal imaging change data of the infrared thermal box images.

[0044] The working principle and technical effect of the above technical solution are as follows: a heater is used to create a thermal simulation environment, generating a high-temperature area and obtaining the hot box and related environmental parameters; while a cooler is used to create a cold simulation environment, forming a low-temperature area and obtaining the cold box and cold simulation environment information. The hot box and cold box simulate extreme high and low temperature environmental conditions, respectively.

[0045] Low-carbon building materials are placed at the junction of the hot box and the cold box, so that one side of the material is in a high-temperature environment (hot box) and the other side is in a low-temperature environment (cold box), thereby building a complete performance simulation and testing system, simulating the temperature difference between the two sides that the material may face in actual application.

[0046] The performance simulation test system controls the temperatures of its hot and cold boxes separately. By precisely adjusting parameters such as the power of the heaters and coolers, the system reaches a predetermined temperature state. Temperature control information from the performance simulation test system, including the set temperature value, actual temperature value, and temperature change rate, is also obtained. Based on this temperature control information, an infrared hot box instrument is used to collect infrared hot box images of low-carbon building materials, recording the surface temperature distribution of the materials at different time points. This thermal imaging change data is then obtained from the infrared hot box images, which reflects the dynamic temperature changes of the materials under the hot and cold simulation environments.

[0047] By constructing a hot simulation environment and a cold simulation environment and placing the material at the connection between the two, the complex working conditions of the material in actual applications where there is a temperature difference on both sides can be simulated with high accuracy, providing a usage scenario that is close to reality.

[0048] By controlling the temperature of the performance simulation testing system and collecting infrared thermal images, we can comprehensively obtain the surface temperature distribution and change data of the material under different temperature conditions. This data not only contains the performance information of the material under stable temperature conditions, but also reflects the dynamic response of the material during temperature changes.

[0049] The infrared heat box diagram displays the temperature distribution on the material surface in an intuitive image form. By comparing the thermal imaging change data at different time points or under different temperature conditions, the temperature differences and change trends of various parts of the material can be clearly observed, thereby intuitively presenting the quality and uniformity of the material's thermal insulation performance.

[0050] Based on the obtained thermal imaging change data, we can deeply analyze the heat transfer mechanism and performance characteristics of the material under different temperature conditions, and discover possible problems such as thermal bridges and heat loss in the material.

[0051] In one embodiment of the present invention, infrared thermal box images of low-carbon building materials are collected according to the temperature control information to obtain thermal imaging change data of the infrared thermal box images, including:

[0052] Obtain temperature control information at each timing node according to a preset timing sequence;

[0053] Acquire temperature difference data of the hot box and the cold box according to the temperature control information at each time series node;

[0054] Obtain the corresponding infrared thermal image based on the temperature difference data at each time series node;

[0055] Obtain temperature control change information of temperature control information at different time series nodes;

[0056] According to the temperature control change information, infrared thermal images of the low-carbon building material at the temperature control change information at different time sequence nodes are acquired, and temperature characteristic infrared thermal images of the temperature control change information at multiple time sequence nodes of the low-carbon building material are obtained.

[0057] The working principle and technical effect of the above technical solution are: according to the pre-set time sequence, the temperature control information of the hot box (hot box) and the cold box at each timing node is obtained in turn. This information reflects the temperature setting and actual status of the hot box and the cold box at different times.

[0058] Based on the temperature control information of each time series node, the temperature difference data between the hot box and the cold box is calculated. The temperature difference data reflects the temperature difference faced by the two sides of the material at different times.

[0059] Based on the temperature difference data at each time node, low-carbon building materials are photographed using an infrared thermal box to obtain infrared thermal images of the temperature difference data corresponding to the time node. These images intuitively present the surface temperature distribution of the material under different temperature difference conditions.

[0060] Analyze the temperature control information at different time series nodes to obtain temperature control change information, including temperature change data.

[0061] Combined with the temperature control change information, the infrared thermal images at different time nodes are sorted and analyzed, and the temperature characteristics that can reflect the material under different temperature control change conditions are extracted, thereby obtaining the temperature characteristic infrared thermal images of multiple time nodes.

[0062] By acquiring data in a preset time sequence, dynamic monitoring of the thermal insulation performance of low-carbon building materials is achieved, which allows clear observation of the performance of materials at different time points and understanding of performance trends over time.

[0063] By calculating the temperature difference data between the hot box and the cold box and correlating it with the infrared thermal image, we can accurately analyze the thermal insulation performance of the material under different temperature difference conditions and clarify the impact of temperature difference on material performance.

[0064] By acquiring temperature control change information and generating temperature characteristic infrared thermal images, we can deeply analyze the impact of factors such as temperature change rate and amplitude on material properties, and discover the performance characteristics and potential problems of materials under different temperature change conditions.

[0065] The obtained infrared thermal images of temperature characteristics at multiple time series nodes can comprehensively and accurately evaluate the thermal insulation performance of low-carbon building materials.

[0066] In one embodiment of the present invention, S2 includes:

[0067] Obtain the temperature characteristic infrared thermal image corresponding to the temperature difference data of 0 as the reference image;

[0068] Obtain the temperature characteristic infrared thermal image corresponding to the maximum value of the temperature difference data as the upper limit image;

[0069] Acquire a temperature characteristic infrared image with temperature difference data between 0 and a maximum value, and obtain a change infrared image;

[0070] The reference image, the changed infrared image and the upper limit image are sorted to obtain a temperature image change sequence;

[0071] The temperature image change data under different temperature difference data are obtained according to the temperature image change sequence.

[0072] The working principle and technical effect of the above technical solution are as follows: from the acquired temperature difference data and the corresponding temperature characteristic infrared thermal image, the image when the temperature difference is 0 is selected as the reference image, representing the temperature distribution of the material under the state of no temperature difference; the image when the temperature difference is maximum is selected as the upper limit image, reflecting the temperature distribution of the material under the maximum temperature difference.

[0073] Acquire the temperature characteristic infrared image with temperature difference between 0 and maximum value, that is, the changing infrared image, which reflects the temperature distribution of the material under different intermediate temperature differences.

[0074] The reference image, the changing infrared image and the upper limit image are sorted in order of temperature difference from small to large to form a temperature image change sequence, which shows the dynamic process of the material temperature distribution changing with the temperature difference.

[0075] Based on the temperature image change sequence, the differences in images under different temperature difference data are analyzed and the temperature image change data is extracted.

[0076] By constructing a temperature image change sequence, the temperature distribution change process of low-carbon building materials under different temperature difference conditions can be clearly and intuitively presented, which is convenient for observation and analysis.

[0077] The baseline image and upper limit image provide key reference points for evaluating material performance, and can accurately locate the performance of the material under no temperature difference and maximum temperature difference conditions.

[0078] The temperature image change data fully reflects the temperature change characteristics of the material under different temperature differences.

[0079] It can accurately evaluate the thermal insulation performance of materials and discover the performance advantages and disadvantages and potential problems of materials under different temperature differences.

[0080] In one embodiment of the present invention, S3 includes:

[0081] Extract temperature distribution features from each temperature image data in the temperature image change sequence to obtain temperature feature extraction data for each temperature image data; the features include connected domain data, uniformity data, high / low temperature area ratio data, etc.

[0082] Acquire change data of temperature feature extraction data of every two adjacent temperature image data in the temperature change sequence to obtain a plurality of adjacent feature change data;

[0083] Calculate the adjacent feature variation coefficient of each two adjacent temperature image data according to each adjacent feature variation data;

[0084] The calculation formula of the adjacent feature variation coefficient is:

[0085]

[0086] Among them, XL is the coefficient of variation of adjacent features, n is the total number of features, is the kth eigenvalue of the i-th temperature image, is the kth eigenvalue of the i+1th temperature image, is the standard deviation of the kth feature in the historical data set, which is not 0;

[0087] Compare each adjacent feature change coefficient with a preset adjacent feature change threshold to obtain an adjacent change comparison result;

[0088] The preset adjacent feature change threshold is the calculation result when the preset maximum allowable standard data is substituted into the formula.

[0089] A plurality of target temperature image data are acquired according to the adjacent change comparison result. Two adjacent temperature image data whose adjacent feature change coefficient is less than a preset adjacent feature change threshold are both target temperature image data.

[0090] The working principle and technical effect of the above technical solution are: for each temperature image data in the temperature image change sequence, its temperature distribution characteristics are extracted to obtain the temperature feature extraction data corresponding to each image. These data can characterize the distribution of temperature in the image.

[0091] The variation of the temperature feature extraction data of every two adjacent temperature image data in the temperature variation sequence is calculated to obtain a plurality of adjacent feature variation data, which reflects the variation degree of the temperature features between adjacent images.

[0092] The adjacent feature variation coefficient of each two adjacent temperature image data is calculated, and the coefficient is used to quantify the relative size of the change in the temperature features of adjacent images.

[0093] Compare each adjacent feature variation coefficient with the preset adjacent feature variation threshold. If the adjacent feature variation coefficient is less than the threshold, the corresponding two adjacent temperature image data are both determined as target temperature image data. Small changes indicate that the material has stabilized during a certain temperature difference change process.

[0094] By calculating the adjacent feature variation coefficient, the degree of temperature feature variation of adjacent temperature image data is quantified, which facilitates intuitive analysis and comparison of the variation differences between different adjacent images, avoiding the complexity of classification and feature comparison and the difficulty in judging images due to inconsistent standards.

[0095] By using preset thresholds to compare the adjacent feature variation coefficients, we can accurately screen out target temperature image data with relatively small temperature feature changes. The data corresponds to relatively stable material properties, avoiding the technical problem of large image errors caused by unstable acquisition materials due to the influence of temperature differences.

[0096] The feature extraction, change calculation and threshold comparison processes are completed automatically, which improves the analysis efficiency and reduces the interference of human factors, making the screening of target temperature image data more accurate and reliable.

[0097] In one embodiment of the present invention, the S4 includes:

[0098] Acquire adjacent temperature image data corresponding to a plurality of adjacent feature change data through the plurality of target temperature images;

[0099] determining that the adjacent temperature image data is thermal balance image data of low-carbon building materials;

[0100] Acquiring a plurality of thermal balance image data from the temperature image change data;

[0101] The temperature difference data corresponding to each thermal balance image data is obtained to obtain the thermal balance temperature difference data.

[0102] The working principle and technical effect of the above technical solution are: obtaining adjacent temperature image data associated with multiple adjacent feature change data corresponding to the multiple screened target temperature images.

[0103] Based on the characteristic that the temperature change of materials is relatively stable in a thermal equilibrium state, these adjacent temperature image data are determined to be thermal equilibrium image data of low-carbon building materials.

[0104] From the temperature image change data, image data determined to be thermally balanced is extracted.

[0105] Find the temperature difference data corresponding to each thermal balance image data, and then obtain the thermal balance temperature difference data.

[0106] By tracing back and judging the target temperature image, the image data when the low-carbon building materials reach the thermal equilibrium state can be accurately located.

[0107] Obtain thermal equilibrium temperature difference data, which is a key indicator for evaluating the thermal insulation performance of materials. It reflects the temperature difference on both sides of the material in a thermal equilibrium state and accurately obtains the data differences caused by heat transfer performance.

[0108] Thermal balance image data and thermal balance temperature difference data provide important support for comprehensive and accurate evaluation of the thermal insulation performance of low-carbon building materials.

[0109] In one embodiment of the present invention, the S5 includes:

[0110] Extracting identical feature data from the thermal balance image data corresponding to the plurality of thermal balance temperature difference data to obtain first thermal balance identical feature data (the identical feature data is a shared identical feature);

[0111] Extracting different feature data from the thermal balance image data corresponding to the plurality of thermal balance temperature difference data to obtain first thermal balance different feature data of the plurality of thermal balance temperature difference data (the different feature data is a feature not common to each thermal balance image data);

[0112] Extract the same feature data from different feature data of the first thermal balance data of multiple thermal balance temperature difference data, and the same feature data from the second thermal balance data (find the same feature data in different feature data, although not all thermal balance image data have the same feature, but some thermal balance image data have the same feature);

[0113] Extracting different characteristic data from first thermal balance data of the plurality of thermal balance temperature difference data to obtain second thermal balance different characteristic data of the plurality of thermal balance temperature difference data;

[0114] Continuously extracting the same feature data and the different feature data from the thermal balance image data corresponding to the multiple thermal balance temperature difference data, to obtain multiple thermal balance same feature data and multiple thermal balance different feature data;

[0115] According to the multiple heat balance identical characteristic data combined with the multiple heat balance different characteristic data, data analysis of the thermal insulation performance of the low-carbon building materials is performed to obtain data analysis results, and the performance status of the thermal insulation performance of the low-carbon building materials is determined according to the data analysis results to obtain performance status determination information.

[0116] The working principle and technical effect of the above technical solution are as follows: first, the common feature data shared by all thermal balance images is extracted from the thermal balance image data; then, the different feature data of each thermal balance image is extracted. Then, within the first thermal balance different feature data, the common feature data shared by some thermal balance images (second thermal balance common feature data) and the remaining different feature data (second thermal balance different feature data) are extracted. This cycle is repeated to continuously extract multiple thermal balance common feature data and different feature data.

[0117] The thermal insulation performance of low-carbon building materials is comprehensively analyzed by combining the extracted multiple thermal balance data with different characteristic data to obtain data analysis results. The thermal insulation performance status of the material is determined based on the results to generate performance status determination information.

[0118] By continuously extracting the same and different feature data at multiple levels, the information contained in the thermal balance image data can be fully and deeply mined without missing any key features that may affect the thermal insulation performance of the material.

[0119] By comprehensively analyzing data with the same and different characteristics, we can more accurately grasp the performance differences of materials under different thermal equilibrium states, clarify which characteristics play a dominant role in thermal insulation performance, and which characteristics are factors that affect performance fluctuations.

[0120] Performance status determination is performed based on comprehensive data analysis results, making the determination results more accurate and comprehensive.

[0121] In one embodiment of the present invention, the data analysis of the thermal insulation performance of the low-carbon building material is performed based on the plurality of thermal balance identical characteristic data in combination with the plurality of thermal balance different characteristic data to obtain the data analysis results, including:

[0122] Sorting the thermal balance data with the same characteristics according to the amount of thermal balance image data corresponding to the thermal balance data with the same characteristics to obtain a sequence of data with the same characteristics; each sequence is sorted for the same characteristic type;

[0123] Sorting the thermal balance non-characteristic feature data according to the amount of thermal balance image data corresponding to different thermal balance feature data to obtain different feature data sequences; each sequence is sorted for the same feature type;

[0124] Calculate thermal insulation performance coefficient based on the same characteristic data sequence combined with different characteristic data sequences;

[0125] The calculation formula of the thermal insulation performance coefficient is:

[0126]

[0127] Among them, GRX is the thermal insulation performance coefficient, α and β are weight data, m is the total number of identical features, n is the total number of different features, w h For the same feature S h The weight of s h is the normalized value of the hth identical feature, v l Different characteristics l The weight of d l is the normalized value of the lth different feature;

[0128] The thermal insulation performance coefficient is compared with a preset performance threshold to obtain a data analysis result of the thermal insulation performance.

[0129] The step of determining the thermal insulation performance of the low-carbon building materials based on the data analysis results to obtain performance status determination information includes:

[0130] When the thermal insulation performance coefficient is greater than the preset performance threshold, the thermal insulation performance of the low-carbon building material is judged to be qualified;

[0131] When the thermal insulation performance coefficient is less than or equal to a preset performance threshold, the thermal insulation performance of the low-carbon building material is judged to be unqualified, and performance status judgment information is obtained.

[0132] The working principle and technical effect of the above technical solution are as follows: based on the amount of thermal balance image data corresponding to the same feature data and different feature data of thermal balance, the same feature data and different feature data are sorted according to the same feature type to obtain the same feature data sequence and different feature data sequence.

[0133] The thermal insulation performance coefficient is calculated by combining the data sequences with the same characteristics and the data sequences with different characteristics. This coefficient comprehensively considers the influence of the same and different characteristics on the thermal insulation performance of the material.

[0134] The calculated thermal insulation performance coefficient is compared with the preset performance threshold. If the coefficient is greater than the threshold, the thermal insulation performance of the material is judged to be qualified; if the coefficient is less than or equal to the threshold, the performance is judged to be unqualified, and finally the performance status judgment information is obtained.

[0135] By calculating the thermal insulation performance coefficient, the thermal insulation performance of the material can be quantified, which facilitates the intuitive and accurate evaluation of the material's performance level.

[0136] By comparing the preset performance threshold with the performance coefficient, it is possible to accurately determine whether the thermal insulation performance of low-carbon building materials is qualified.

[0137] Based on scientific performance determination results, corresponding solutions can be obtained to improve the overall thermal insulation performance of the building and reduce energy consumption.

[0138] In one embodiment of the present invention, the system includes:

[0139] The simulation temperature control detection module is used to build a performance simulation detection system, perform temperature control on the performance simulation detection system, obtain temperature difference data at each time series node, and then obtain temperature characteristic infrared images under temperature control change information at different time series nodes;

[0140] A change sequence acquisition module is used to acquire a temperature image change sequence based on temperature difference data;

[0141] A stable change target acquisition module is used to obtain adjacent feature change coefficients of adjacent temperature image data according to the temperature image change sequence, and obtain target temperature image data according to the adjacent feature change coefficients;

[0142] A thermal balance temperature difference acquisition module is used to acquire thermal balance image data based on adjacent temperature image data, and further acquire thermal balance temperature difference data;

[0143] The performance analysis module is used to obtain multiple levels of identical feature data and different feature data through the thermal balance image data corresponding to the thermal balance temperature difference data, and then obtain identical feature data sequences and different feature data sequences, calculate the thermal insulation performance coefficient, perform thermal insulation performance status judgment, and obtain performance status judgment information.

[0144] The working principle and technical effect of the above technical solution are as follows: a performance simulation detection system is built, and the system's temperature is precisely controlled to simulate the temperature environment under different actual working conditions. During system operation, the temperature difference data of each time series node is recorded, and infrared imaging technology is used to obtain infrared images of the temperature characteristics corresponding to the temperature control changes. The images intuitively show the surface temperature distribution of the material under different temperature conditions.

[0145] Based on the acquired temperature difference data, the temperature characteristic infrared images at different time nodes are arranged according to the size of the temperature difference to form a temperature image change sequence, which reflects the dynamic process of the material surface temperature changing with the temperature difference.

[0146] Adjacent temperature image data in a temperature image change sequence is analyzed to calculate adjacent feature variation coefficients. These coefficients quantify the characteristic differences between adjacent images, such as the degree of change in temperature distribution. By analyzing these coefficients, target temperature image data that remains stable after a temperature change (temperature changes can lead to significant temperature differences between the two sides of the material, resulting in image instability, so the image is stable). This data can highlight important features of the material during its stable state.

[0147] Based on the adjacent temperature image data, the image data when the material reaches thermal equilibrium is further analyzed, i.e., the thermal equilibrium image data. At the same time, the corresponding thermal equilibrium temperature difference data is obtained. This data reflects the temperature difference between the two sides of the material in the thermal equilibrium state and is an important reference for evaluating the thermal insulation performance of the material. The thermal equilibrium state here means that although there is a temperature difference between the two sides of the material, the influence of the temperature on the internal temperature of the material has stabilized. At this time, the image of the material is relatively accurate.

[0148] From the thermal balance image data corresponding to the thermal balance temperature difference data, multiple levels of identical feature data (and different feature data) are extracted to form identical feature data sequences and different feature data sequences, respectively. By comprehensively analyzing these feature data sequences, the thermal insulation performance coefficient is calculated. Finally, the thermal insulation performance status is determined based on the coefficient value to obtain performance status determination information.

[0149] By building a simulation detection system and acquiring data with multiple time series and multiple temperature differences, we can fully simulate the various temperature environments in which the material will be used in actual use, accurately evaluate the thermal insulation performance of the material under different temperature differences, and avoid the limitations brought by a single test condition, preventing the temperature difference on both sides from affecting the material, resulting in inaccurate infrared images and, in turn, inaccurate analysis results.

[0150] The analysis of the temperature image change sequence and the adjacent feature change coefficients can dynamically reflect the detailed process of the temperature difference change on the material surface, provide an in-depth understanding of the material's performance at different temperature stages, and discover potential performance problems or advantages.

[0151] Screening target temperature image data and extracting feature data at different levels can accurately locate the normal and abnormal characteristics of the material's thermal insulation performance, enhancing the accuracy and efficiency of material performance analysis.

[0152] The thermal insulation performance coefficient is calculated based on the comprehensive characteristic data and the performance status is determined, which further makes the determination of the material performance status more accurate and efficient.

[0153] Obviously, those skilled in the art may make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if such modifications and variations fall within the scope of the claims and their equivalents, the present invention is intended to include such modifications and variations.

Claims

1. A method for testing the thermal insulation performance of low-carbon building materials, characterized in that: The method comprises: S1. Build a performance simulation detection system, perform temperature control on the performance simulation detection system, obtain temperature difference data at each time series node, and then obtain temperature characteristic infrared images under temperature control change information at different time series nodes; S2. Obtaining a temperature image change sequence based on the temperature difference data; S3, obtaining adjacent feature variation coefficients of adjacent temperature image data according to the temperature image variation sequence, and obtaining target temperature image data according to the adjacent feature variation coefficients; S4. Acquire thermal balance image data based on adjacent temperature image data, and then acquire thermal balance temperature difference data; S5. Obtain multiple levels of identical feature data and different feature data through the thermal balance image data corresponding to the thermal balance temperature difference data, and then obtain identical feature data sequences and different feature data sequences, calculate the thermal insulation performance coefficient, perform thermal insulation performance status determination, and obtain performance status determination information.

2. The method for detecting thermal insulation performance of low-carbon building materials according to claim 1, characterized in that: Said S1 comprises: A thermal simulation environment is constructed by a heater to obtain information about the hot box and the thermal simulation environment; A cold simulation environment is constructed by a refrigerator to obtain cold box and cold simulation environment information; Placing the low-carbon building material at the connection point of a hot box and a cold box to obtain a performance simulation detection system; Perform temperature control of the hot box and cold box of the performance simulation detection system to obtain temperature control information of the performance simulation detection system; Infrared thermal box images of low-carbon building materials are collected according to the temperature control information to obtain thermal imaging change data of the infrared thermal box images.

3. The method for detecting thermal insulation performance of low-carbon building materials according to claim 2, characterized in that: Collecting infrared thermal box images of low-carbon building materials according to the temperature control information to obtain thermal imaging change data of the infrared thermal box images includes: Obtain temperature control information at each timing node according to a preset timing sequence; Acquire temperature difference data of the hot box and the cold box according to the temperature control information at each time series node; Obtain the corresponding infrared thermal image based on the temperature difference data at each time series node; Obtain temperature control change information of temperature control information at different time series nodes; According to the temperature control change information, infrared thermal images of the low-carbon building material at the temperature control change information at different time sequence nodes are acquired, and temperature characteristic infrared thermal images of the temperature control change information at multiple time sequence nodes of the low-carbon building material are obtained.

4. The method for detecting thermal insulation performance of low-carbon building materials according to claim 1, characterized in that: The S2 includes: Obtain the temperature characteristic infrared thermal image corresponding to the temperature difference data of 0 as the reference image; Obtain the temperature characteristic infrared thermal image corresponding to the maximum value of the temperature difference data as the upper limit image; Acquire a temperature characteristic infrared image with temperature difference data between 0 and a maximum value, and obtain a change infrared image; The reference image, the changed infrared image and the upper limit image are sorted to obtain a temperature image change sequence; The temperature image change data under different temperature difference data are obtained according to the temperature image change sequence.

5. The method for detecting thermal insulation performance of low-carbon building materials according to claim 1, characterized in that: The S3 includes: Extracting temperature distribution features of each temperature image data in the temperature image change sequence to obtain temperature feature extraction data of each temperature image data; Acquire change data of temperature feature extraction data of every two adjacent temperature image data in the temperature change sequence to obtain a plurality of adjacent feature change data; Calculate the adjacent feature variation coefficient of each two adjacent temperature image data according to each adjacent feature variation data; Compare each adjacent feature change coefficient with a preset adjacent feature change threshold to obtain an adjacent change comparison result; A plurality of target temperature image data are acquired according to the adjacent change comparison result.

6. The method for detecting thermal insulation performance of low-carbon building materials according to claim 1, characterized in that: The S4 includes: Acquire adjacent temperature image data corresponding to a plurality of adjacent feature change data through the plurality of target temperature images; determining that the adjacent temperature image data is thermal balance image data of low-carbon building materials; Acquiring a plurality of thermal balance image data from the temperature image change data; The temperature difference data corresponding to each thermal balance image data is obtained to obtain the thermal balance temperature difference data.

7. The method for detecting thermal insulation performance of low-carbon building materials according to claim 1, characterized in that: The S5 includes: Extracting identical feature data from thermal balance image data corresponding to the plurality of thermal balance temperature difference data to obtain first thermal balance identical feature data; Extracting different feature data from the thermal balance image data corresponding to the plurality of thermal balance temperature difference data to obtain first thermal balance different feature data of the plurality of thermal balance temperature difference data; Extracting the same characteristic data from different characteristic data of the first thermal balance data of the plurality of thermal balance temperature difference data, and extracting the same characteristic data from the second thermal balance data; Extracting different characteristic data from first thermal balance data of the plurality of thermal balance temperature difference data to obtain second thermal balance different characteristic data of the plurality of thermal balance temperature difference data; Continuously extracting the same feature data and the different feature data from the thermal balance image data corresponding to the multiple thermal balance temperature difference data, to obtain multiple thermal balance same feature data and multiple thermal balance different feature data; According to the multiple heat balance identical characteristic data combined with the multiple heat balance different characteristic data, data analysis of the thermal insulation performance of the low-carbon building materials is performed to obtain data analysis results, and the performance status of the thermal insulation performance of the low-carbon building materials is determined according to the data analysis results to obtain performance status determination information.

8. The method for detecting thermal insulation performance of low-carbon building materials according to claim 1, characterized in that: The step of performing data analysis on the thermal insulation performance of the low-carbon building material based on the plurality of thermal balance identical characteristic data in combination with the plurality of thermal balance different characteristic data to obtain the data analysis results includes: Sorting the thermal balance data with the same characteristics according to the amount of thermal balance image data corresponding to the thermal balance data with the same characteristics to obtain a sequence of data with the same characteristics; Sorting the thermal balance non-characteristic feature data according to the amount of thermal balance image data corresponding to different thermal balance feature data to obtain different feature data sequences; Calculate thermal insulation performance coefficient based on the same characteristic data sequence combined with different characteristic data sequences; The thermal insulation performance coefficient is compared with a preset performance threshold to obtain a data analysis result of the thermal insulation performance.

9. A method for testing the thermal insulation performance of low-carbon building materials according to claim 8, characterized in that: The step of determining the thermal insulation performance of the low-carbon building materials based on the data analysis results to obtain performance status determination information includes: When the thermal insulation performance coefficient is greater than the preset performance threshold, the thermal insulation performance of the low-carbon building material is judged to be qualified; When the thermal insulation performance coefficient is less than or equal to a preset performance threshold, the thermal insulation performance of the low-carbon building material is judged to be unqualified, and performance status judgment information is obtained.

10. A system for implementing the method for detecting thermal insulation performance of low-carbon building materials according to claim 1, characterized in that: The system comprises: The simulation temperature control detection module is used to build a performance simulation detection system, perform temperature control on the performance simulation detection system, obtain temperature difference data at each time series node, and then obtain temperature characteristic infrared images under temperature control change information at different time series nodes; A change sequence acquisition module is used to acquire a temperature image change sequence based on temperature difference data; A stable change target acquisition module is used to obtain adjacent feature change coefficients of adjacent temperature image data according to the temperature image change sequence, and obtain target temperature image data according to the adjacent feature change coefficients; A thermal balance temperature difference acquisition module is used to acquire thermal balance image data based on adjacent temperature image data, and further acquire thermal balance temperature difference data; The performance analysis module is used to obtain multiple levels of identical feature data and different feature data through the thermal balance image data corresponding to the thermal balance temperature difference data, and then obtain identical feature data sequences and different feature data sequences, calculate the thermal insulation performance coefficient, perform thermal insulation performance status judgment, and obtain performance status judgment information.

Images