Method and system for detecting thermal insulation performance of composite wallboard

Abstract

The invention discloses a thermal insulation performance detection method and system for a composite wallboard, and relates to the technical field of building material detection, and the method comprises the steps: dividing a composite wallboard sample into a plurality of grids, setting an inversion starting point for each grid according to initial data, starting an inversion algorithm to gradually adjust the heat conductivity coefficient of each grid, and generating a heat conductivity coefficient distribution diagram, calculating the average heat conductivity coefficient and the overall thermal resistance value of the sample; the test platform applies full-automatic thermal excitation and sound wave excitation to a sample, collects sound signals and thermal imaging data related to internal defects of the composite wallboard, calculates the optimal matching time point of the sound signals and the thermal imaging data, and extracts defect related characteristics in the sound signals and temperature anomaly characteristics in thermal imaging in a double-flow processing mode. Generating a comprehensive defect distribution diagram, and comparing the comprehensive defect distribution diagram with the heat conductivity coefficient distribution diagram and the average heat conductivity coefficient to obtain preliminary judgment; through acoustic-thermal multi-mode cooperative detection, the spatial distribution of tiny defects in a sample is accurately positioned, and the influence of the tiny defects on the heat-conducting property is judged.

Description

Technical Field

[0001] The present invention relates to the technical field of building material testing, and particularly to a method and system for detecting the thermal insulation performance of composite wall panels. Background Art

[0002] The technology for detecting the thermal insulation performance of composite wall panels has developed rapidly with the increasing demand for building energy conservation. Early detection methods generally measure the heat flux density and overall thermal resistance under a constant temperature difference, and have been widely used in the evaluation of thermal conductivity.

[0003] Traditional methods have made good progress in detecting the thermal insulation performance of composite wall panels, but there are still many problems. First, the heat flow meter method takes the overall thermal resistance and average thermal conductivity as the main outputs, and cannot capture the non-uniformity of the thermal conductivity caused by local thermal bridges or micro defects, resulting in a distorted evaluation of the internal performance distribution of composite wall panels. Second, existing defect detection technologies mostly adopt isolated modes. Infrared thermal imaging can only capture the surface thermal distribution and is difficult to quantify the depth and size of internal defects. Although acoustic emission technology can identify interface delamination, it lacks the correlation analysis with thermal conduction characteristics, resulting in the lack of multi-dimensional data support for defect evaluation. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a method for detecting the thermal insulation performance of composite wall panels to solve the problems that existing defect detection technologies mostly adopt isolated modes, resulting in the lack of multi-dimensional data support for defect evaluation and the inability to capture the non-uniformity of the thermal conductivity caused by micro defects.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: In a first aspect, the present invention provides a method for detecting the thermal insulation performance of composite wall panels, which includes: Select a composite wall panel sample, build a test platform, and collect the initial data of the composite wall panel sample through the test platform. The initial data includes the heat flux density distribution, the temperature distributions on both sides, and the surface temperature image. Divide the composite wall panel sample into several grids, set an inversion starting point for each grid according to the initial data, and start the inversion algorithm to gradually adjust the thermal conductivity of each grid, generate a thermal conductivity distribution map, and calculate the average thermal conductivity and the overall thermal resistance value of the sample. The test platform applies full-automatic thermal excitation and acoustic excitation to the sample, collects the acoustic signals and thermal imaging data related to the internal defects of the composite wall panel, calculates the best matching time point of the two, and respectively extracts the defect-related features in the acoustic signals and the temperature anomaly features in the thermal imaging through a dual-stream processing method to generate a comprehensive defect distribution map, and compare it with the thermal conductivity distribution map and the average thermal conductivity to obtain a preliminary judgment. Generate a performance evaluation report for the composite wall panel through dynamic thermal impedance spectroscopy analysis technology.

[0007] As a preferred solution of the thermal insulation performance detection method for the composite wall panel described in the present invention, wherein: assign an initial thermal conductivity assumption value to each grid according to the initial data as the starting point of the inversion calculation, start the non-uniform material parameter inversion algorithm, divide the grid into multiple groups, and generate a three-dimensional thermal conductivity distribution map according to the calculation results of each group.

[0008] As a preferred solution of the thermal insulation performance detection method for the composite wall panel described in the present invention, wherein: obtain the average thermal conductivity and the overall thermal resistance value of the composite wall panel sample according to the thermal conductivity distribution map, the heat flux density distribution, and the temperature distribution on both sides.

[0009] As a preferred solution of the thermal insulation performance detection method for the composite wall panel described in the present invention, wherein: through the attention mechanism, enhance the defect-related features in the acoustic signal and the temperature anomaly features in the thermal imaging respectively extracted by the dual-stream processing method, and generate a comprehensive defect distribution map.

[0010] As a preferred solution of the thermal insulation performance detection method for the composite wall panel described in the present invention, wherein: compare with the thermal conductivity distribution map and the average thermal conductivity to obtain a preliminary judgment, which specifically includes the following steps. Select the data of each defect point from the defect distribution map and estimate the thermal conductivity of the defect point. Set a deviation threshold, calculate the deviation percentage of the thermal conductivity of the defect point from the average thermal conductivity, and compare the obtained deviation percentage with the deviation threshold to obtain a preliminary judgment.

[0011] As a preferred solution of the thermal insulation performance detection method for the composite wall panel described in the present invention, wherein: apply a periodic heat flux excitation to the composite wall panel sample to obtain heat flux excitation data and temperature response data, and perform a fast Fourier transform to calculate the amplitude and phase of the heat flux excitation data and the temperature response data. Calculate according to the amplitude of the heat flux excitation data and the temperature response data to obtain the thermal impedance value.

[0012] As a preferred solution of the thermal insulation performance detection method for the composite wall panel described in the present invention, wherein: generate a thermal impedance spectrum based on the thermal impedance value, and combine the defect distribution map to locate the position of the defect. Calculate according to the thermal impedance spectrum and the overall thermal resistance value to verify the accuracy of the analysis and generate a performance evaluation report for the composite wall panel.

[0013] In a second aspect, the present invention provides a thermal insulation performance detection system for a composite wall panel, including Building module: Select a composite wallboard sample, build a test platform, and collect the initial data of the composite wallboard sample through the test platform; Calculation module: Divide the composite wallboard sample into several grids, set the inversion starting point for each grid according to the initial data, and start the inversion algorithm to gradually adjust the thermal conductivity of each grid, generate a thermal conductivity distribution map, and calculate the average thermal conductivity and the overall thermal resistance value of the sample; Testing module: The test platform applies full-automatic thermal excitation and acoustic excitation to the sample, collects the acoustic signals and thermal imaging data related to the internal defects of the composite wallboard, calculates the best matching time point of the two, and respectively extracts the defect-related features in the acoustic signal and the temperature anomaly features in the thermal imaging through a dual-stream processing method, generates a comprehensive defect distribution map, and compares it with the thermal conductivity distribution map and the average thermal conductivity to obtain a preliminary judgment; Evaluation module: Generate a performance evaluation report of the composite wallboard through dynamic thermal impedance spectroscopy analysis technology.

[0014] In a third aspect, the present invention provides a computer device, including a memory and a processor, where the memory stores a computer program, and: when the computer program is executed by the processor, any step of the method for detecting the heat preservation performance of the composite wallboard as described in the first aspect of the present invention is implemented.

[0015] In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, and: when the computer program is executed by the processor, any step of the method for detecting the heat preservation performance of the composite wallboard as described in the first aspect of the present invention is implemented.

[0016] The beneficial effects of the present invention are as follows: By refining the composite wallboard sample into grids and combining the parallel calculation of the non-uniform material parameter inversion algorithm, the precise adjustment of the thermal conductivity of each grid and the generation of a three-dimensional thermal conductivity distribution map are realized, and at the same time, the average thermal conductivity and the overall thermal resistance value are calculated. The non-uniformity of the internal thermal conductivity performance of the sample is revealed, and a quantitative index is provided for the overall evaluation of the heat preservation performance. Finally, the tiny heat conduction differences can be visually presented, significantly improving the detection accuracy and the comprehensiveness of the performance evaluation of non-uniform composite materials. In addition, through acoustic-thermal multi-modal collaborative detection, the spatial distribution of tiny defects inside the sample is accurately located, and its influence on the thermal conductivity performance is preliminarily judged. Compared with traditional single thermal imaging or acoustic wave detection, it can more accurately identify deep defects and quantify their influence, providing a highly reliable basis for subsequent performance evaluation. Description of the Drawings

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0018] Figure 1 It is a flowchart of the method for detecting the thermal insulation performance of the composite wallboard in Embodiment 1.

[0019] Figure 2 It is a module diagram of the system for detecting the thermal insulation performance of the composite wallboard in Embodiment 1.

[0020] Figure 3 It is a schematic diagram for generating mesh division and thermal conductivity distribution in Embodiment 1.

[0021] Figure 4 It is a schematic diagram of acoustic-thermal multimodal detection in Embodiment 1. Specific Embodiments

[0022] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following will give a detailed description of the specific embodiments of the present invention with reference to the accompanying drawings of the specification.

[0023] Many specific details are set forth in the following description to facilitate a thorough understanding of the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0024] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that can be included in at least one implementation manner of the present invention. The "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selectively exclusive embodiment from other embodiments.

[0025] Embodiment 1, referring to Figures 1 to 4 , is the first embodiment of the present invention. This embodiment provides a method for detecting the thermal insulation performance of a composite wallboard, including the following steps: S1. Select a composite wallboard sample, build a test platform, and collect the initial data of the composite wallboard sample through the test platform.

[0026] Specifically, it includes the following steps. Select a composite wallboard sample of 40 cm × 40 cm × 6 cm from the inventory of composite wallboards to be tested, such as a three-layer structure of steel plate - polyurethane - rock wool, to ensure its representativeness and practicability. At the same time, check the surface of the composite wallboard sample to ensure that there are no obvious scratches, dents or damages. At the same time, use a straightedge and a level to measure the flatness of the composite wallboard sample, and require that the surface height difference does not exceed 0.2 mm to avoid errors in subsequent heat conduction tests.

[0027] Fix the composite wallboard sample on a specially built test platform. The test platform is a sturdy stainless steel frame structure, equipped with a variety of high-precision detection devices, including a high-temperature side heating plate (temperature range can be adjusted to 60 °C, temperature control accuracy ±0.5 °C, with pulse heating function, and supports sine wave modulation), a low-temperature side cooling plate (temperature can be adjusted to 0 °C, accuracy ±0.3 °C), a heat flux sensor array (consisting of 16 measuring points, distributed in a 4×4 pattern, resolution 0.01 W / m²), 4 acoustic emission sensors (frequency range 20 kHz - 1 MHz, sensitivity 0.1 mV / Pa), an infrared thermal imager (thermal sensitivity 0.03 °C, field of view 25°×19°) and an automatic acoustic wave excitation device (consisting of 10 electromagnetic drive vibration heads, distributed at intervals of 10 cm, each vibration head excitation force 5 N, frequency 1 Hz). Place the composite wallboard sample in the test area of the test platform and fix it using a pair of pneumatic clamping devices. The contact surfaces of the clamping devices are covered with silicone pads to ensure close contact between the sample and the high-temperature side heating plate and the low-temperature side cooling plate, and to avoid uneven heat conduction caused by air gaps.

[0028] S1.1. The test platform starts the high-precision detection devices, sets the temperature of the high-temperature side heating plate to 50 °C, and the temperature of the low-temperature side cooling plate to 10 °C, and waits for the temperature field to stabilize (about 30 minutes, temperature difference fluctuation less than 0.1 °C). The heat flux sensor array records the heat flux density data (unit: W / m²) on the surface of the sample. At the same time, use thermocouples to record the temperature distribution data on both sides of the composite wallboard sample (accurate to 0.05 °C), and the infrared thermal imager collects the initial temperature distribution image on the surface of the composite wallboard sample. The data acquisition unit collects all data at a frequency of 1 Hz for 5 minutes and takes the average value. These initial data include heat flux density distribution, temperature distribution on both sides, and surface temperature image.

[0029] Preferably, by building a test platform equipped with a heat flux sensor array, acoustic emission sensors and an infrared thermal imager, this method realizes the synchronous acquisition of multi-modal data of heat, sound and infrared. It improves the comprehensiveness of the acquired data, can not only measure the heat conduction characteristics, but also provides a basis for subsequent acoustic-thermal defect detection and delamination analysis.

[0030] S2. Divide the composite wallboard sample into several grids, set the inversion starting point for each grid according to the initial data, and start the inversion algorithm to gradually adjust the thermal conductivity of each grid, generate a thermal conductivity distribution map, and calculate the average thermal conductivity and overall thermal resistance of the sample.

[0031] Specifically, it includes the following steps: Transfer the initial data to the computer. The computer divides the composite wallboard sample into 1000 small grids (in a three-dimensional grid layout of 10×10×10) through a uniform grid division algorithm. Each grid corresponds to a local area inside the sample, with a size of 4 cm×4 cm×0.6 cm. This division method refines the 40 cm×40 cm×6 cm volume of the sample into multiple independent units for analyzing their thermal conductivity characteristics one by one.

[0032] The computer assigns an initial assumed value of thermal conductivity to each grid based on the input heat flux density distribution, temperature distributions on both sides, and surface temperature image as the starting point for inversion calculation. Extract the thermal distribution characteristics of the surface temperature image through image processing technology. The assumed value of thermal conductivity is based on the typical properties of the sample material (for example, about 50 W / m·K for steel plates, about 0.02 W / m·K for polyurethane, and about 0.04 W / m·K for rock wool), and is preliminarily adjusted in combination with the thermal distribution characteristics extracted from the surface temperature image to be closer to the actual conditions.

[0033] The computer starts the non-uniform material parameter inversion algorithm. The non-uniform material parameter inversion algorithm gradually adjusts the thermal conductivity of each grid by comparing the actually measured heat flux density distribution and temperature distributions on both sides with the heat flux and temperature differences predicted by the assumed values. Specifically, the algorithm adaptively determines the intensity of adjustment according to the change of heat flux density in the local area (for example, areas where the heat flux suddenly increases or decreases). For example, it enhances the adjustment amplitude for areas with drastic changes (such as material interfaces) and weakens the adjustment for stable areas to ensure more accurate calculation of the thermal conductivity in non-uniform areas.

[0034] Use the calculation module to take over the calculation task of the non-uniform material parameter inversion algorithm. Divide the 1000 grids into multiple groups, for example, 10 groups with 100 grids in each group; and assign them to multiple computing cores of the GPU for simultaneous processing. Each group independently calculates the thermal conductivity of the responsible area and updates the results in real time. After the calculation task is completed, receive and integrate the calculation results of all groups to generate a three-dimensional thermal conductivity distribution map. The three-dimensional thermal conductivity distribution map visually shows the thermal conductivity values (unit: W / m·K) of each grid inside the composite wallboard sample, and automatically marks the abnormal areas (such as thermal bridges or material defects) through color coding. The color coding can use red to represent high thermal conductivity and blue to represent low thermal conductivity, which can intuitively reflect the non-uniformity.

[0035] Preferably, through the refined division of 1000 grids and the generation of a three-dimensional thermal conductivity distribution map, this method can accurately capture the thermal conductivity differences in different regions within the sample (such as steel plates, polyurethane, and rock wool layers), especially at the material junctions or defect areas, and the detection resolution far exceeds that of traditional methods. This enables the method to discover tiny thermal bridges or material defects that are easily overlooked, providing higher precision for the evaluation of thermal insulation performance.

[0036] Based on the generated thermal conductivity distribution map, as well as the heat flux density distribution and the temperature distributions on both sides, the average thermal conductivity and the overall thermal resistance of the sample are further calculated. The average thermal conductivity is obtained by taking the average of the thermal conductivities of 1000 grids, and the overall thermal resistance is obtained by dividing the thickness of the composite wallboard sample, which is 6 cm, by the average thermal conductivity. These indicators provide a quantitative basis for the overall thermal insulation performance of the composite wallboard sample, complementing the local information in the distribution map.

[0037] Furthermore, the three-dimensional thermal conductivity distribution map is stored in a visual form and the abnormal areas are marked by color coding, allowing users to intuitively identify the positions of thermal bridges or defects. This output form facilitates technicians to quickly understand the thermal insulation performance distribution of the sample, reduces the analysis difficulty, and at the same time provides a clear reference for subsequent defect location and optimization.

[0038] S3. The test platform applies full-automatic thermal excitation and acoustic excitation to the sample, collects the acoustic signals and thermal imaging data related to the internal defects of the composite wallboard, calculates the best matching time point of the two, and respectively extracts the defect-related features in the acoustic signals and the temperature anomaly features in the thermal imaging through a dual-stream processing method to generate a comprehensive defect distribution map, and compares it with the thermal conductivity distribution map and the average thermal conductivity to obtain a preliminary judgment.

[0039] Specifically, it includes the following steps: The test platform receives the generated thermal conductivity distribution map, combines the marked abnormal areas (such as thermal bridges or material defect locations), further detects the tiny defects (such as air bubbles or delamination) inside the sample, and initiates a full-automatic excitation and acoustic-thermal multimodal data acquisition process. A pulse heater is started on one side of the composite wallboard sample and operated for 10 seconds to quickly raise the temperature of one side of the sample to 55 °C, and then the heater is turned off to allow the sample to cool naturally to the ambient temperature (for example, 25 °C). The thermal excitation process stimulates the thermal response of the internal defects of the sample through short-term high temperature; at the same time, the test platform activates the automatic acoustic excitation device and uses the vibrating head to periodically strike the surface of the sample for 10 seconds. The purpose is to induce the acoustic response of the internal defects of the sample through mechanical vibration.

[0040] The acoustic emission sensor records the acoustic signals generated by the vibration head knocking and thermal excitation. The acquisition time is 20 seconds, the sampling rate is 10 kHz, and audio data containing acoustic wave information related to defects is generated. The infrared thermal imager records the dynamic temperature change images on the surface of the sample. The acquisition time is 30 seconds, and a sequence of thermal images containing temperature information related to defects is generated. The data acquisition unit automatically synchronizes and stores the acoustic signals and thermal imaging data in the computer, and unifies the timestamps of the two sets of data.

[0041] The computer receives the acoustic signals (20-second audio data) and thermal imaging data (30-second video sequence) transmitted by the data acquisition unit, and starts the processing program to begin the analysis. Then, by analyzing the frequency changes of the acoustic signals and the temperature fluctuations of the thermal imaging, the computer calculates the optimal matching time point between the two to ensure the precise alignment of the acoustic signals and thermal imaging data in terms of time and space, so as to avoid feature deviations caused by different propagation speeds of acoustic waves and heat (acoustic waves are fast, heat is slow) and acquisition time differences (20 seconds vs 30 seconds). Then, the computer adopts a two-stream processing method to extract defect-related features such as amplitude mutations (acoustic wave responses indicating defects) from the acoustic signals respectively. For example, 50 amplitude mutation points (time points and amplitude values) are identified, and features such as local high or low temperature points (thermal responses indicating defects) are extracted from the thermal imaging data. For example, 200 abnormal pixels (coordinates and temperature values) are located.

[0042] S3.1. Preset an attention mechanism beforehand and set rules based on defect characteristics. For example, for acoustic signals, weights are assigned according to the relative magnitude of the change in amplitude mutation instead of a fixed threshold. Specifically: Calculate the mean value of the change amplitudes of all mutation points (for example, the average change of 50 points is 0.8 mV). The weight formula is: weight = min(1.0, amplitude change / mean × 0.9), with a range of 0.1 - 1.0.

[0043] Example: For mutation point 1, the change is 0.9 mV and the mean is 0.8 mV, weight = min(1.0, 0.9 / 0.8 × 0.9) = 1.0. For mutation point 2, the change is 0.4 mV and the mean is 0.8 mV, weight = min(1.0, 0.4 / 0.8 × 0.9) = 0.45. Such a method can make the mutations with larger amplitudes more prominent (such as weight 1.0), and the weights of smaller amplitudes are reduced (such as 0.45), highlighting key defects.

[0044] The adjustment rule for thermal imaging is to assign weights according to the relative magnitude of the temperature deviation, rather than a fixed threshold. The weight formula is: weight = min(1.0, deviation / mean × 0.95), with a range of 0.1 - 1.0.

[0045] For example, for anomaly point 1, the deviation is 10°C, the mean value is 6°C, and the weight = min(1.0, 10 / 6 × 0.95) = 1.0. For anomaly point 2: the deviation is 3°C, the mean value is 6°C, and the weight = min(1.0, 3 / 6×0.95) = 0.475. Similar to the harmonic signal, points with larger deviations can be made more prominent (such as a weight of 1.0), and the weights of points with smaller deviations are reduced (such as 0.475), optimizing the defect saliency.

[0046] Preferably, the method preset a dynamic weight rule in advance, adjusts the weight according to the mean value of the current feature set, highlights the significant features, and suppresses the secondary signals. The dynamic weight rule has self - adaptability, can dynamically optimize the feature saliency according to the characteristics of the composite wallboard sample, and improve the detection accuracy.

[0047] S3.2. Enhance the saliency of the features of the acoustic signal and the thermal imaging data through the set attention mechanism. For the acoustic signal features, calculate the mean value of the amplitude changes of all mutation points (such as 0.8 mV). If a certain point changes by 0.9 mV, the weight is 1.0; if it changes by 0.4 mV, the weight is 0.45. For the thermal imaging features, calculate the mean temperature deviation of all anomaly points (such as 6°C). If a certain point has a deviation of 10°C, the weight is 1.0; if the deviation is 3°C, the weight is 0.475. Thus, the significant defect features are highlighted (such as the amplitude of 0.9 mV is enhanced to 0.9 mV, and the temperature of 60°C is enhanced to 60°C), and the secondary features are suppressed (such as the amplitude of 0.4 mV is reduced to 0.18 mV, and the temperature deviation of 3°C is reduced to 1.425°C), improving the clarity of the defect signal.

[0048] Fuse the extracted and enhanced acoustic signal features and thermal imaging features through a fusion algorithm, map them to the spatial coordinate system of the sample, and generate a comprehensive defect distribution map. This map clearly marks the positions and sizes of the tiny defects inside the sample (such as a 0.2 - mm air bubble) in a two - dimensional or three - dimensional form through spatial coordinates and color coding (such as red indicating a severe defect area).

[0049] Select the data of each defect point from the defect distribution map. For example, for a certain defect point A with coordinates (x1, y1, z1), the amplitude of the acoustic signal changes suddenly by 1 mV, and the thermal imaging temperature is 60 °C. For a certain defect point B with coordinates (x2, y2, z2), the amplitude changes suddenly by 0.4 mV, and the temperature is 52 °C. These features are the results of fusion, reflecting the anomalies of the defect in the acoustic wave and thermal responses. Estimate the thermal conductivity at the defect using the acoustic-thermal characteristics through thermal response inversion. Subsequently, calculate the percentage deviation of the local thermal conductivity from the average value. The formula is: deviation % = |(local thermal conductivity - average thermal conductivity) / average thermal conductivity| × 100%. The larger the deviation, the more significant the impact of the defect on the overall thermal conductivity. Set a deviation threshold. When the deviation > 50%, it indicates a significant deviation, suggesting that the defect seriously affects the heat insulation performance. When the deviation < 10%, it indicates a slight impact, suggesting that the defect has a minor impact on the overall performance. The specific value of the deviation threshold can be customized according to the usage scenario and requirements.

[0050] Preferably, this method extracts local high-temperature or low-temperature points from thermal imaging through acoustic-thermal dual-stream processing and attention mechanism, extracts sudden amplitude changes from acoustic signals, and fuses them to generate a three-dimensional defect distribution map, while comparing with the average thermal conductivity. The in-depth analysis and quantitative evaluation from the surface to the interior significantly improve the accuracy of defect detection.

[0051] S4. Generate a performance evaluation report for the composite wallboard through dynamic thermal impedance spectroscopy analysis technology.

[0052] Specifically, it includes the following steps: Through the sine wave modulation function of the high-temperature side heating plate, starting from a low frequency of 0.01 Hz, gradually increasing to 10 Hz (step size 0.05 Hz), apply a periodic heat flux excitation to the composite wallboard sample. The heat flux amplitude is kept constant at 10 W / m². Each frequency lasts for 5 cycles (for example, at 0.01 Hz, the period is 100 seconds, and 5 cycles are 500 seconds; at 10 Hz, the period is 0.1 second, and 5 cycles are 0.5 seconds) to ensure that the internal thermal response of the sample reaches a stable state. The dynamic heat flux excitation can stimulate the thermal resistance characteristics of each layer and defects. Subsequently, record the temperature change data of each layer through thermocouples, that is, the temperature response data. Save the applied heat flux excitation data (frequency, amplitude) and the temperature response data of each layer (recorded by 8 thermocouples, sampling rate 100 Hz, for example, at 0.01 Hz, 50,000 points are generated in 500 seconds).

[0053] S4.1. For each frequency, the computer extracts 5-cycle stable data. For example, at 0.01 Hz, take 500 seconds of complete data; at 10 Hz, take 0.5 seconds of complete data to ensure that the thermal response has reached a steady state. Use timestamps to match the heat flux excitation data with the temperature response data of 8 thermocouples to ensure the accuracy of the corresponding relationship.

[0054] Extract the 5-cycle stable data, perform fast Fourier transform on the heat flux and the temperatures of 8 thermocouples, calculate the amplitudes and phases of the heat flux excitation data and the temperature response data of each thermocouple. For example, at 0.01 Hz, the heat flux amplitude is 10 W / m², the temperature amplitude of thermocouple 1 is 0.5 °C, and the phase difference is 30°. Then calculate the thermal impedance value, which is equal to the amplitude of the temperature response data divided by the amplitude of the heat flux excitation data.

[0055] Subsequently, integrate the thermal impedance values at 200 frequency points to generate 8 thermal impedance spectrum curves (corresponding to 8 interfaces, which are also the thermal impedance spectra). Each curve reflects the relationship between the frequency (0.01 Hz - 10 Hz) and the thermal resistance amplitude. For example, for the steel - polyurethane interface, the low-frequency thermal resistance is 0.05 m²·K / W and the high-frequency thermal resistance is 0.01 m²·K / W; for the polyurethane - rock wool interface, the low-frequency thermal resistance is 0.08 m²·K / W and the high-frequency thermal resistance is 0.02 m²·K / W, revealing the response ability of each layer to the dynamic heat flux. For example, the thermal resistance is higher at low frequencies because the heat flux penetrates deeper, and the thermal resistance is lower at high frequencies because it only affects the surface.

[0056] For the abnormal regions marked in the defect distribution map, such as a 0.2 mm air bubble at coordinates x1, y1, z1, focus on analyzing its abnormal performance in the thermal impedance spectrum. For example, an increase in the thermal resistance amplitude indicates that the defect hinders the heat flux, and the thermal resistance may increase from 0.5 m²·K / W to 1.0 m²·K / W, or there is a delay in the response time, and the temperature change at the defect is delayed by 0.1 seconds. By comparing the thermal impedance spectra of the normal regions, locate the interlayer position where the defect is located (such as the polyurethane - rock wool interface).

[0057] Preferably, this method captures the dynamic thermal resistance characteristics through sinusoidal heat flux excitation and thermal impedance spectrum analysis, not only measures the total thermal resistance, but also decomposes the thermal resistance of each layer and the defect response, with stronger dynamic analysis ability, and is applicable to the performance evaluation of complex composite materials.

[0058] Extract the average thermal resistance of each layer from the thermal impedance spectrum, and integrate the average thermal resistances of all layers to calculate the total thermal resistance of the sample (such as 2.0 m²·K / W), and compare it with the previously obtained overall thermal resistance value (such as 2.1 m²·K / W) to verify the accuracy of the layer-by-layer analysis. A smaller deviation (such as within 5%) indicates reliable analysis; if the deviation is large, check whether the data is incorrect. This verification ensures that the layer-by-layer results are consistent with the overall performance.

[0059] Combined with the defect distribution map, analyze the degree of weakening of the overall thermal insulation performance by defects. For example, at a certain defect, the thermal resistance drops by 0.5 m²·K / W, and the total thermal resistance drops from 2.0 m²·K / W to 1.5 m²·K / W, with a percentage decrease of 25%. This assessment quantifies the impact degree of the defect (such as the thermal bridge causing 25% of the thermal insulation failure). Integrate the analysis results and output a comprehensive report, including the stratified thermal resistance distribution (thermal resistance value curves or charts for each layer), defect impacts (location, interlayer positioning, percentage of thermal resistance drop), as well as the average thermal conductivity (such as 0.05 W / m·K) and the overall thermal resistance value (such as 2.1 m²·K / W). The report is presented in a visual form, such as a 3D thermal resistance map + table, intuitively showing the thermal insulation performance of the composite wallboard and providing suggestions for optimization, such as strengthening the thickness of the polyurethane layer to compensate for the defect impact.

[0060] Furthermore, through the dynamic thermal impedance spectroscopy technology, high-precision stratified thermal resistance analysis, micro-defect positioning and impact quantification, multi-frequency dynamic analysis, overall and stratified verification, and visual report output have been achieved, providing comprehensive, efficient, and accurate support for the thermal insulation performance evaluation and optimization of composite wallboards.

[0061] This embodiment also provides a thermal insulation performance detection system for a composite wallboard, including: A building module that selects a composite wallboard sample, builds a test platform, and collects the initial data of the composite wallboard sample through the test platform; A calculation module that divides the composite wallboard sample into several grids, sets the inversion starting point for each grid according to the initial data, and starts the inversion algorithm to gradually adjust the thermal conductivity of each grid, generates a thermal conductivity distribution map, and calculates the average thermal conductivity and the overall thermal resistance value of the sample; A test module that the test platform applies full-automatic thermal excitation and acoustic excitation to the sample, collects the acoustic signals and thermal imaging data related to the internal defects of the composite wallboard, calculates the best matching time point of the two, and respectively extracts the defect-related features in the acoustic signals and the temperature anomaly features in the thermal imaging through a dual-stream processing method, generates a comprehensive defect distribution map, and compares it with the thermal conductivity distribution map and the average thermal conductivity to obtain a preliminary judgment; An evaluation module that generates a performance evaluation report for the composite wallboard through the dynamic thermal impedance spectroscopy analysis technology.

[0062] This embodiment also provides a computer device applicable to the case of the thermal insulation performance detection method for a composite wallboard, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the thermal insulation performance detection method for a composite wallboard as proposed in the above embodiment.

[0063] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a carrier network, NFC (Near Field Communication), or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or may be a button, a trackball, or a touchpad provided on the housing of the computer device, or may also be an external keyboard, touchpad, or mouse, etc.

[0064] This embodiment also provides a storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the method for detecting the heat preservation performance of a composite wallboard as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as a static random access memory (Static Random Access Memory, abbreviated as SRAM), an electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, abbreviated as EEPROM), an erasable programmable read-only memory (Erasable Programmable Read Only Memory, abbreviated as EPROM), a programmable read-only memory (Programmable Red-Only Memory, abbreviated as PROM), a read-only memory (Read-Only Memory, abbreviated as ROM), a magnetic memory, a flash memory, a magnetic disk, or an optical disc.

[0065] In summary, the present invention: refines the composite wallboard sample into a grid, combines the non-uniform material parameter inversion algorithm for parallel calculation, realizes the precise adjustment of the thermal conductivity of each grid and the generation of a three-dimensional thermal conductivity distribution map, and simultaneously calculates the average thermal conductivity and the overall thermal resistance value. It reveals the non-uniformity of the internal thermal conductivity of the sample and provides a quantitative index for the overall evaluation of the thermal insulation performance. Finally, the tiny thermal conduction differences can be visually presented, significantly improving the detection accuracy and the comprehensiveness of the performance evaluation of non-uniform composite materials. In addition, through the acoustic-thermal multi-modal collaborative detection, the spatial distribution of tiny defects inside the sample can be accurately located, and the influence of the defects on the thermal conductivity can be preliminarily judged. Compared with the traditional single thermal imaging or acoustic wave detection, it can more accurately identify deep defects and quantify their influence, providing a highly reliable basis for subsequent performance evaluation.

[0066] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A method for detecting the heat insulation performance of a composite wallboard, characterized in that: including Select a composite wallboard sample, build a test platform, and collect the initial data of the composite wallboard sample through the test platform. The initial data includes heat flux density distribution, temperature distributions on both sides, and surface temperature images. Divide the composite wallboard sample into several grids, set the inversion starting point for each grid according to the initial data, and start the inversion algorithm to gradually adjust the thermal conductivity of each grid, generate a thermal conductivity distribution map, and calculate the average thermal conductivity and overall thermal resistance of the sample. The test platform applies full-automatic thermal excitation and acoustic excitation to the sample, collects acoustic signals and thermal imaging data related to internal defects of the composite wallboard, calculates the best matching time point between the two, and respectively extracts defect-related features in the acoustic signal and temperature anomaly features in the thermal imaging through a two-stream processing method to generate a comprehensive defect distribution map, and compare it with the thermal conductivity distribution map and the average thermal conductivity to obtain a preliminary judgment. Generate a performance evaluation report for the composite wallboard through dynamic thermal impedance spectroscopy analysis technology.

2. The method for detecting the heat insulation performance of the composite wallboard according to claim 1, characterized in that: Assign an initial thermal conductivity assumption value to each grid according to the initial data as the starting point for inversion calculation. Start the non-uniform material parameter inversion algorithm, divide the grid into multiple groups, and generate a three-dimensional thermal conductivity distribution map based on the calculation results of each group.

3. The method for detecting the heat insulation performance of the composite wallboard according to claim 2, characterized in that: Obtain the average thermal conductivity and overall thermal resistance of the composite wallboard sample based on the thermal conductivity distribution map, heat flux density distribution, and temperature distributions on both sides.

4. The method for detecting the heat insulation performance of the composite wallboard according to claim 3, wherein: Enhance the extraction of defect-related features in the acoustic signal and temperature anomaly features in the thermal imaging respectively using the two-stream processing method through an attention mechanism to generate a comprehensive defect distribution map.

5. The method for detecting the heat insulation performance of the composite wallboard according to claim 4, wherein: The step of comparing with the thermal conductivity distribution map and the average thermal conductivity to obtain a preliminary judgment specifically includes the following steps Select the data of each defect point from the defect distribution map and estimate the thermal conductivity of the defect point. Set a deviation threshold, calculate the deviation percentage between the thermal conductivity of the defect point and the average thermal conductivity, and compare the obtained deviation percentage with the deviation threshold to obtain a preliminary judgment.

6. The method for detecting the heat insulation performance of the composite wallboard according to claim 5, wherein: Apply a periodic heat flux excitation to the composite wallboard sample to obtain heat flux excitation data and temperature response data, and perform a fast Fourier transform to calculate the amplitudes and phases of the heat flux excitation data and the temperature response data. Calculate based on the amplitudes of the heat flux excitation data and the temperature response data to obtain the thermal impedance value.

7. The method for detecting the heat insulation performance of the composite wallboard according to claim 1, characterized in that: Generate a thermal impedance spectrum based on the thermal impedance value and locate the position of the defect in combination with the defect distribution map. Calculate based on the thermal impedance spectrum and the overall thermal resistance to verify the accuracy of the analysis and generate a performance evaluation report for the composite wallboard.

8. An insulation performance detection system for a composite wall panel, based on the insulation performance detection method of the composite wall panel according to any one of claims 1 to 7, characterized in that: including A building module that selects a composite wallboard sample, builds a test platform, and collects the initial data of the composite wallboard sample through the test platform. A calculation module that divides the composite wallboard sample into several grids, sets the inversion starting point for each grid according to the initial data, and starts the inversion algorithm to gradually adjust the thermal conductivity of each grid, generates a thermal conductivity distribution map, and calculates the average thermal conductivity and overall thermal resistance of the sample. Test module. The test platform applies full-automatic thermal excitation and acoustic excitation to the sample, collects acoustic signals and thermal imaging data related to internal defects of the composite wallboard, calculates the best matching time point between the two, and respectively extracts defect-related features in the acoustic signal and temperature anomaly features in the thermal imaging through a dual-stream processing method, generates a comprehensive defect distribution map, and compares it with the thermal conductivity distribution map and the average thermal conductivity to obtain a preliminary judgment; Evaluation module. Generate a performance evaluation report of the composite wallboard through dynamic thermal impedance spectroscopy analysis technology.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that: When the processor executes the computer program, the steps of the thermal insulation performance detection method of the composite wallboard according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, the steps of the thermal insulation performance detection method of the composite wallboard according to any one of claims 1 to 7 are implemented.

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