Fabricated insulation board fireproof performance detection method and system

Abstract

The invention discloses a method and a system for detecting fireproof performance of an assembled insulation board, and relates to the technical field of fireproof material detection.The method comprises the following steps: preparing an assembled insulation board sample, and performing constant-temperature and constant-humidity balance and ultraviolet aging grouping treatment to obtain a standardized insulation board sample interface; scanning the interface of the standardized insulation board sample by using terahertz time-domain spectroscopy to generate an interface debonding distribution diagram and a defect coordinate list; ablating detection points of the fabricated insulation board sample through laser-induced breakdown spectroscopy to obtain plasma temperature, analyzing element migration data of the flame retardant, and calculating the residual rate of the flame retardant; inputting the interface debonding distribution diagram and the flame retardant residual rate into a coupling failure model, and judging a synergistic failure type of interface debonding and flame retardant attenuation; and generating a three-dimensional visualization scheme according to the synergistic failure type of flame retardation attenuation, and obtaining a process optimization report. According to the invention, through the process optimization report output by the three-dimensional risk field, the fire resistance of the insulation board in an extreme environment is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of fireproof material detection, in particular to a method and system for detecting the fireproof performance of assembled insulation boards. Background Technique

[0002] With the continuous improvement of building energy efficiency standards, assembled insulation boards, as an important building material, have been widely used. Depending on simple tests under laboratory conditions, such as combustion tests, although they can provide a certain evaluation of fireproof performance, their ability to analyze internal structural defects, flame retardant distribution, and long-term weather resistance is limited. With the development of non-destructive testing techniques, terahertz time-domain spectroscopy and laser-induced breakdown spectroscopy have gradually been applied to material analysis, providing a basis for in-depth exploration of the microstructure and chemical composition of insulation boards.

[0003] In the technical field of fireproof material detection, when evaluating the fireproof performance of insulation boards, the key factor of interface debonding on the overall performance is often ignored, and it is impossible to accurately identify and locate the interface debonding area, resulting in difficulty in comprehensively evaluating the fireproof safety of insulation boards in actual applications. 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 fireproof performance of assembled insulation boards, which solves the problem that the interface debonding area cannot be accurately identified and located, thus affecting the comprehensive evaluation of the fireproof safety of insulation boards.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: In the first aspect, the present invention provides a method for detecting the fireproof performance of assembled insulation boards, which includes preparing an assembled insulation board sample, performing constant temperature and humidity equilibration and ultraviolet aging grouping treatment to obtain a standardized insulation board sample interface, using terahertz time-domain spectroscopy to scan the standardized insulation board sample interface to generate an interface debonding distribution map and a defect coordinate list; Ablating the detection points of the assembled insulation board sample by laser-induced breakdown spectroscopy to obtain the plasma temperature, analyzing the flame retardant element migration data, and calculating the flame retardant residue rate; Inputting the interface debonding distribution map and the flame retardant residue rate into a coupled failure model to determine the type of collaborative failure of interface debonding and flame retardant attenuation; Generating a three-dimensional visualization scheme according to the type of collaborative failure of flame retardant attenuation and obtaining a process optimization report.

[0007] As a preferred embodiment of the fire performance detection method for the prefabricated thermal insulation board of the present invention, the following steps are included: preparing a sample of the prefabricated thermal insulation board, and performing constant temperature and humidity equilibration and ultraviolet aging grouping treatment to obtain a standardized interface of the thermal insulation board sample. Prepare a sample of the prefabricated thermal insulation board using the factory standard process; Place the sample of the prefabricated thermal insulation board in a constant temperature and humidity chamber for equilibration treatment to obtain a prefabricated thermal insulation board sample after internal stress elimination and equilibration; Divide the prefabricated thermal insulation board sample after internal stress elimination and equilibration into an experimental group and a control group; Use a QUV accelerated aging instrument to continuously irradiate the experimental group, and keep the control group away from light to obtain a standardized interface of the thermal insulation board sample.

[0008] As a preferred embodiment of the fire performance detection method for the prefabricated thermal insulation board of the present invention, the following steps are included: using terahertz time-domain spectroscopy to scan the standardized interface of the thermal insulation board sample to generate an interface debonding distribution map and a list of defect coordinates. Use transmission terahertz time-domain spectroscopy to perform a planar grid scan on the standardized interface of the thermal insulation board sample to obtain the time-domain reflection signal of each detection point, and extract the signal characteristic parameters through a signal processing algorithm; Use the wavelet transform time-domain analysis method to extract the reflection time delay, amplitude attenuation, and signal-to-noise ratio from the signal characteristic parameters, and set the reflection time delay threshold, amplitude attenuation threshold, and signal-to-noise ratio threshold; When the reflection time delay, amplitude attenuation, and signal-to-noise ratio are all greater than the reflection time delay threshold, amplitude attenuation threshold, and signal-to-noise ratio threshold, obtain the defect area; Use the ellipse fitting algorithm to calculate the center coordinates and equivalent diameter of the defect area, and generate an interface debonding distribution map and a list of defect coordinates.

[0009] As a preferred embodiment of the fire performance detection method for the prefabricated thermal insulation board of the present invention, the following steps are included: ablating the detection points of the prefabricated thermal insulation board sample by laser-induced breakdown spectroscopy to obtain the plasma temperature. Use laser-induced breakdown spectroscopy to ablate the detection points of the prefabricated thermal insulation board to obtain the original spectral data, and obtain the intensity ratio of the characteristic spectral lines of the flame retardant elements from the original spectral data through the peak fitting algorithm; Use the double spectral line intensity ratio method to calculate the plasma temperature of the intensity ratio of the characteristic spectral lines of the flame retardant elements.

[0010] As a preferred embodiment of the fire performance detection method for the prefabricated thermal insulation board of the present invention, the following steps are included: analyzing the migration data of the flame retardant elements and calculating the flame retardant residue rate. Based on the plasma temperature, a temperature-decomposition degree relationship model is established using the Arrhenius equation. The plasma temperature is input into the temperature-decomposition degree relationship model to calculate the thermal decomposition ratio of the flame retardant; According to the thermal decomposition ratio of the flame retardant, the intensity of the characteristic spectral line of phosphorus element is calibrated through a dynamic correction formula; Correct the spectral line intensity of phosphorus element according to the thermal decomposition ratio of the flame retardant; Compare the corrected spectral line intensity of phosphorus element with the standard intensity of phosphorus element to obtain the actual residual rate of the flame retardant.

[0011] As a preferred scheme of the method for detecting the fire resistance performance of the prefabricated insulation board described in the present invention, wherein: input the interface debonding distribution map and the residual rate of the flame retardant into the coupling failure model to determine the type of collaborative failure of interface debonding and flame retardant attenuation, including the following steps, Adopt the ICP point cloud registration algorithm to spatially match the interface debonding distribution map and the residual rate of the flame retardant, and establish a dataset of characteristic parameters of detection points; Use the dataset of characteristic parameters of detection points to train the coupling failure model, and calculate the failure coupling coefficient through a bivariate coupling equation; Calculate the deviation between the actual residual rate of the flame retardant and the predicted residual rate of the flame retardant through the relative deviation analysis method; Use the weighted fusion method to combine the deviation of the residual rate of the flame retardant with the corrected spectral line intensity of phosphorus element to obtain the thermal degradation value; When the failure coupling coefficient is less than the critical debonding area, it is determined as the interface debonding dominant failure; When the failure coupling coefficient is greater than the thermal degradation value, it is determined as the thermal degradation dominant type; When the failure coupling coefficient is less than or equal to the critical debonding area and greater than or equal to the thermal degradation value, it is determined as the composite failure type.

[0012] As a preferred scheme of the method for detecting the fire resistance performance of the prefabricated insulation board described in the present invention, wherein: generate a three-dimensional visualization scheme according to the type of collaborative failure of flame retardant attenuation, and obtain a process optimization report, including the following steps, Based on the type of collaborative failure, use the Kriging interpolation algorithm to construct a three-dimensional risk field, and obtain the thermal degradation area, interface debonding area and composite failure area through the Gaussian kernel function; Through the Pearson analysis of the three-dimensional risk field and the actual residual rate of the flame retardant, obtain the fire resistance performance index; When the fire resistance performance index is greater than the thermal degradation area, interface debonding area and composite failure area at the same time, obtain a three-dimensional visualization scheme; According to the three-dimensional visualization scheme, verify through COMSOL multi-physics field simulation to obtain a process optimization report.

[0013] In a second aspect, the present invention provides an assembled thermal insulation board fire performance detection system, including a coordinate list module, preparing assembled thermal insulation board samples, performing constant temperature and humidity balance and ultraviolet aging grouping treatment to obtain a standardized thermal insulation board sample interface, using terahertz time-domain spectroscopy to scan the standardized thermal insulation board sample interface, and generating an interface debonding distribution map and a defect coordinate list; A residue rate module, ablating the detection points of the assembled thermal insulation board sample by laser-induced breakdown spectroscopy to obtain the plasma temperature, analyzing the migration data of flame retardant elements, and calculating the flame retardant residue rate; A failure type module, inputting the interface debonding distribution map and the flame retardant residue rate into a coupled failure model to determine the collaborative failure type of interface debonding and flame retardant attenuation; An optimization scheme module, generating a three-dimensional visualization scheme according to the collaborative failure type of flame retardant attenuation and obtaining a process optimization report.

[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 assembled thermal insulation board fire performance detection method 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 assembled thermal insulation board fire performance detection method described in the first aspect of the present invention is implemented.

[0016] The beneficial effects of the present invention are as follows: Through the collaborative detection of terahertz time-domain spectroscopy and laser-induced breakdown spectroscopy, a multi-dimensional and accurate evaluation of the fire performance of assembled thermal insulation boards is realized. Combining with the ellipse fitting algorithm, the detection accuracy of interface debonding defects is improved. Laser-induced breakdown spectroscopy calculates the plasma temperature by the double spectral line intensity ratio method. Combining with the Arrhenius equation and the dynamic nitrogen-phosphorus ratio correction model, it can effectively distinguish normal pyrolysis from abnormal migration. Through spatial matching, collaborative analysis is achieved. Based on the failure coupling coefficient formula, the chain failure mechanism between interface defects and flame retardant migration is quantitatively revealed for the first time. Through the process optimization report output by the three-dimensional risk field, the fire resistance performance of the thermal insulation board in extreme environments is improved, breaking through the technical bottleneck that traditional detection technologies cannot synchronously analyze interface structures and element migrations, and providing an integrated solution for assembled buildings from microscopic defect identification to macroscopic performance prediction. 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 drawings required for the description of the embodiments. Obviously, the 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 drawings can also be obtained based on these drawings.

[0018] Figure 1 It is a flowchart of the fire performance detection method for prefabricated insulation panels.

[0019] Figure 2 It is a schematic diagram of the fire performance detection system for prefabricated insulation panels.

[0020] Figure 3 It is a schematic diagram of the residual rate of flame retardant.

[0021] Figure 4 It is a schematic diagram for determining the type of synergistic failure of interface debonding and flame retardancy attenuation. Specific Embodiments

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

[0023] In the following description, many specific details are set forth to fully understand 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 generalizations 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 phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment that is mutually exclusive with other embodiments.

[0025] Refer to Figures 1 to 4 , this embodiment provides a fire performance detection method for prefabricated insulation panels, including the following steps: S1. Prepare prefabricated insulation panel samples, and perform constant temperature and humidity balance and ultraviolet aging grouping treatment to obtain a standardized insulation panel sample interface.

[0026] S1.1. Prepare prefabricated insulation panel samples from prefabricated insulation panels using factory standard processes.

[0027] Furthermore, precise batching and mixing are carried out through an automated production line, and a continuous hot pressing and forming process is adopted to compound each layer of materials into an integral sheet under set temperature and pressure conditions. After forming, a CNC numerical control cutting center is used to precisely cut the sheet according to standard dimensions to ensure that the flatness of the sample edge and the dimensional tolerance meet the detection requirements. Finally, the surface of the sample is cleaned, and a laser flatness detector is used to inspect the quality of the sample, and unqualified products with defects such as bubbles, delamination, or deformation are removed to ensure that the physical properties and appearance quality of all samples meet the standard requirements.

[0028] S1.2. Place the assembled thermal insulation board samples in a thermostatic and humidistatic chamber for equilibration treatment to obtain the assembled thermal insulation board samples after the internal stress is eliminated and balanced.

[0029] Furthermore, place the samples vertically on the sample rack at a standard spacing to ensure uniform air circulation. Set the temperature inside the chamber to the standard laboratory ambient temperature, and control the relative humidity within the range required by the building materials testing specification. Then gradually adjust to the target temperature and humidity. Use a high-precision temperature and humidity sensor to monitor the environmental parameters inside the chamber in real time, and track the quality change by the sample weighing method. When the sample mass change rate shows less than the deviation of the thermal insulation board sample in three consecutive measurements, it is determined that the sample has reached the humidity equilibrium state. At the same time, use a strain gauge to monitor the dimensional stability of the sample. After confirming that the internal stress is completely released, a structurally stable equilibration-treated sample is obtained.

[0030] S1.3. Divide the assembled thermal insulation board samples after the internal stress is eliminated and balanced into an experimental group and a control group.

[0031] Furthermore, for the experimental group: Place the assembled thermal insulation board samples after equilibration in a QUV accelerated aging tester and conduct continuous irradiation according to the standard aging test conditions (such as a temperature of 60 °C, a UV irradiation intensity of 0.89 W / m² @ 340 nm, and periodic spraying) to simulate the long-term environmental aging effect.

[0032] For the control group: Store the samples of the same batch and the same equilibration treatment in a light-shielded thermostatic and humidistatic environment (such as 23 °C ± 2 °C, relative humidity 50% ± 5%) to avoid any interference from light or temperature and humidity fluctuations as a reference standard. The stratified random sampling method is adopted to ensure the statistical consistency of the physical performance parameters between the experimental group and the control group. The thickness distribution of each sample is measured using a digital thickness gauge, the surface temperature uniformity is detected by an infrared thermal imager, and the mass is weighed using an electronic balance. The initial performance data are recorded. Based on the principle of multi-dimensional parameter matching, automatic pairing is carried out through computer-aided grouping to ensure that the differences in key indicators such as thermal conductivity, density, and moisture content between the two groups of samples do not exceed the allowable range. After grouping, a laser marking machine is used to uniquely number the samples, and a complete sample file is established to record the initial state parameters. The two groups of samples are stored in independent environmental control areas to avoid cross-contamination, providing a comparable reference sample group for subsequent experiments.

[0033] S1.4: The experimental group is continuously irradiated using a QUV accelerated weathering tester, and the control group is treated in the dark to obtain a standardized insulation board sample interface.

[0034] Furthermore, when the experimental group samples are continuously irradiated using a QUV accelerated weathering tester, the ultraviolet spectral irradiation conditions are set according to the ISO4892-3 standard. The UVA-340 lamp is selected to simulate the ultraviolet band of sunlight, and a periodic spraying device is configured to simulate the natural dew condensation effect. The irradiation intensity, chamber temperature, and relative humidity parameters are precisely adjusted through a closed-loop control device to ensure that the temperature rise and fall curve of each aging cycle conforms to the characteristics of the actual environment change. The control group samples are stored in a constant temperature and dark environment, completely shielded using ultraviolet-resistant packaging materials, and maintained at the same environmental temperature and humidity baseline as the experimental group. During the treatment process, samples are regularly taken for testing, and the surface performance changes are monitored using devices such as a color difference meter and a gloss meter. When the experimental group samples reach the predetermined aging degree, the treatment is terminated, and finally a standardized sample interface system with clear aging difference characteristics is obtained.

[0035] S2: Use terahertz time-domain spectroscopy to scan the standardized insulation board sample interface to generate an interface debonding distribution map and a defect coordinate list.

[0036] S2.1: Use a transmission terahertz time-domain spectroscopy to perform a planar grid scan on the standardized insulation board sample interface to obtain the time-domain reflection signal of each detection point, and extract the signal characteristic parameters through a signal processing algorithm.

[0037] Furthermore, use a transmission terahertz time-domain spectroscopy system to perform a planar grid scan on the standardized insulation board sample interface to ensure that the detection point spacing is evenly distributed. After each detection point collects the time-domain waveform signal, noise reduction and baseline correction processing are performed through a digital signal processor. The fast Fourier transform is used to convert the time-domain signal into frequency-domain characteristics, and the fast Fourier transform method is used to extract characteristic parameters including the main peak amplitude, phase shift, and spectral energy distribution in the frequency-domain characteristics.

[0038] S2.2. Extract the reflection time delay, amplitude attenuation, and signal-to-noise ratio from the signal characteristic parameters using the wavelet transform time-domain analysis method, and set the reflection time delay threshold, amplitude attenuation threshold, and signal-to-noise ratio threshold.

[0039] Furthermore, denoise and normalize the signal characteristic parameters to obtain a clean time-domain waveform as the input. Decompose the preprocessed signal characteristic parameters into time-frequency components of different scales through wavelet transform, extract the high-frequency detail coefficients, calculate the reflection time delay using the time difference between wave peaks in the high-frequency components, and record its value. Calculate the amplitude attenuation based on the energy ratio of the incident wave and the reflected wave reconstructed by wavelet, combined with the reflection time delay result. Calculate the signal-to-noise ratio based on the energy ratio of the noise component and the effective component of the signal after wavelet decomposition. Set the determination thresholds for the reflection time delay, amplitude attenuation, and signal-to-noise ratio respectively based on historical data or material properties.

[0040] S2.3. When the reflection time delay, amplitude attenuation, and signal-to-noise ratio are all greater than the reflection time delay threshold, amplitude attenuation threshold, and signal-to-noise ratio threshold respectively, obtain the defect area.

[0041] Furthermore, perform wavelet multi-scale decomposition on the original signal to obtain time-domain components of different frequency bands. Locate the time difference between the reflection wave peaks through the decomposed time-domain components to calculate the reflection time delay. Compare the energy ratio of the incident wave and the reflected wave in the characteristic frequency band to quantify the amplitude attenuation rate. Use the energy ratio of the effective component and the residual noise of the signal reconstructed by wavelet coefficients to obtain the signal-to-noise ratio. When the reflection time delay, amplitude attenuation, and signal-to-noise ratio all exceed the determination thresholds of the reflection time delay, amplitude attenuation, and signal-to-noise ratio, it is determined as the defect area.

[0042] S2.4. Use the ellipse fitting algorithm to calculate the center coordinates and equivalent diameter of the defect area, and generate an interface debonding distribution map and a defect coordinate list.

[0043] Furthermore, for the identified defect area, first perform morphological opening operation to eliminate small noises, and then use the least squares method to fit the defect boundary points to the standard ellipse equation. Obtain the geometric parameters of the ellipse such as the center coordinates, major axis, minor axis, and rotation angle through iterative optimization calculation. Integrate the geometric feature information of all defects and output a two-dimensional interface debonding distribution map with coordinate annotations and a defect coordinate list including defect size, position.

[0044] S3. Ablate the detection points of the assembled thermal insulation board samples by laser-induced breakdown spectroscopy to obtain the plasma temperature.

[0045] S3.1. Use laser-induced breakdown spectroscopy to ablate the detection points of the assembled thermal insulation board to obtain the original spectral data, and obtain the intensity ratio of the characteristic spectral lines of the flame retardant elements from the original spectral data through the peak fitting algorithm.

[0046] Furthermore, when using laser-induced breakdown spectroscopy to ablate the detection points of the assembled thermal insulation board, a high-energy pulsed laser is focused on the sample surface. The adaptive baseline correction algorithm is used to eliminate the influence of continuous background radiation, and the peak position accuracy is improved by Lorentz broadening correction to obtain the intensity ratio of the characteristic spectral lines of the flame retardant elements.

[0047] S3.2. Use the double spectral line intensity ratio method to calculate the plasma temperature of the intensity ratio of the characteristic spectral lines of the flame retardant elements. The expression is ; where T e is the plasma temperature, in K B is the Boltzmann constant, E ion is the ionization energy, I1 is the atomic spectral line intensity, I2 is the ionic spectral line intensity, A2 is the radiation coefficient of the second spectral line, A1 is the radiation coefficient of the first spectral line, g1 is the statistical weight of the upper energy level corresponding to the first spectral line, g2 is the statistical weight of the upper energy level corresponding to the second spectral line, λ1 is the wavelength of the first spectral line, and λ2 is the wavelength of the second spectral line.

[0048] Furthermore, the atomic spectral line intensity and the ionic spectral line intensity are obtained by measuring the spectrum emitted by the flame retardant in the plasma environment with a spectrometer. The double spectral line intensity ratio method is used to calculate the plasma temperature by measuring the atomic spectral line intensity and the ionic spectral line intensity of the flame retardant element characteristics, combining the radiation coefficients of the corresponding spectral lines, the statistical weights of the upper energy levels corresponding to the spectral lines, and the wavelengths of the spectral lines. Based on the thermodynamic relationship between the ionic spectral line intensity and the energy level distribution, the internal energy distribution state of the plasma is deduced from the atomic spectral line intensity, so as to accurately reflect the plasma temperature and provide a quantitative basis for analyzing the behavior mechanism of the flame retardant elements in the high-temperature environment. S4. Analyze the migration data of the flame retardant elements and calculate the residual rate of the flame retardant.

[0049] S4.1. Based on the plasma temperature, use the Arrhenius equation to establish a temperature-decomposition relationship model. Input the plasma temperature into the temperature-decomposition relationship model to calculate the thermal decomposition ratio of the flame retardant. The expression is ; where α is the thermal decomposition ratio of the flame retardant, D is the ammonium polyphosphate flame retardant, E a is the ammonium polyphosphate flame retardant.

[0050] Furthermore, first, under different plasma temperature conditions, the corresponding material decomposition degree data are measured through simulation. The collected plasma temperature and decomposition degree data are preprocessed, denoised and smoothed. Based on ammonium polyphosphate flame retardant and ammonium polyphosphate flame retardant, as thermal decomposition kinetic parameters, through plasma temperature, as the temperature input variable, a temperature-decomposition degree relationship model is established by constructing a thermal decomposition kinetic expression based on the Arrhenius equation. The least squares method is used to optimize the temperature-decomposition degree relationship model to obtain the quantitative relationship between temperature and decomposition degree. The accuracy and reliability of the temperature-decomposition degree relationship model are evaluated through goodness-of-fit test and cross-validation means. A temperature-decomposition degree relationship model is established, and the relationship between temperature and decomposition rate is described by the Arrhenius equation. Considering the thermal decomposition characteristic parameters of ammonium polyphosphate flame retardant, the measured plasma temperature is substituted into the established temperature-decomposition degree relationship model, and the numerical iteration method is used to solve the nonlinear equation to calculate the thermal decomposition ratio of the flame retardant in different temperature regions. The temperature decomposition degree relationship model fully considers the competition mechanism of the molecular chain breakage and cross-linking reaction of the flame retardant and can accurately reflect the chemical bond breakage process under thermal action.

[0051] S4.2. Calibrate the intensity of the characteristic spectral line of phosphorus element according to the thermal decomposition ratio of the flame retardant through the dynamic correction formula. The expression is

[0052] where NP R is the intensity of the characteristic spectral line of phosphorus element after calibration, and NP R0 is the initial nitrogen-phosphorus ratio.

[0053] Furthermore, according to the thermal decomposition ratio of the flame retardant at different temperatures, the actual release amount of phosphorus element is dynamically evaluated. Based on the correction factor of the thermal decomposition ratio of the flame retardant, the correction factor of the thermal decomposition ratio of the flame retardant is introduced into the dynamic correction formula to obtain the intensity of the characteristic spectral line of phosphorus element after calibration, so as to realize the real-time correction and accurate reflection of the spectral line intensity of phosphorus element.

[0054] S4.3. Correct the intensity of the phosphorus element spectral line according to the thermal decomposition ratio of the flame retardant. The expression is ; where I p,corrected is the corrected intensity of the phosphorus element spectral line, and I p,measured is the measured intensity of the phosphorus element.

[0055] Furthermore, measure the thermal decomposition ratio of the flame retardant under different temperature conditions, establish a quantitative relationship between temperature and decomposition degree, determine the corresponding phosphorus element release ratio in combination with the actual temperature or thermal decomposition state of the detection point, introduce a correction factor based on the decomposition ratio, dynamically adjust the intensity of the characteristic spectral line of phosphorus element obtained by the original measurement, divide the original spectral line intensity by the actual decomposition ratio of the corresponding detection point, compensate for the detection deviation of phosphorus element caused by incomplete thermal decomposition of the material, so as to obtain an accurate phosphorus element content to reflect the material state.

[0056] S4.4. Compare the corrected phosphorus element spectral line intensity with the standard phosphorus element intensity to obtain the actual flame retardant residue rate. The expression is ; where R is the actual flame retardant residue rate, and I p,standard is the standard phosphorus element intensity.

[0057] Furthermore, when calculating the actual flame retardant residue rate, select the standard phosphorus element intensity of the same batch without aging treatment as the reference. By normalizing the corrected phosphorus element spectral line intensity and the standard phosphorus element intensity of the same batch, an accurate residue rate is obtained, eliminating the influence brought by instrument fluctuations and sample inhomogeneity, and being able to accurately reflect the retention of the flame retardant in the actual use environment.

[0058] S5. Based on the actual flame retardant residue rate, establish a coupled failure model, input the interface debonding distribution map and the flame retardant residue rate into the coupled failure model, and determine the type of synergistic failure of interface debonding and flame retardant attenuation.

[0059] S5.1. Use the ICP point cloud registration algorithm to spatially match the interface debonding distribution map and the flame retardant residue rate, and establish a detection point characteristic parameter data set.

[0060] Furthermore, discretize the interface debonding distribution map into a point cloud data set, extract the spatial coordinates and characteristic values. Take the interface debonding distribution map as the source point cloud, use the ICP algorithm to iteratively calculate the nearest neighbor point pairs, and complete the spatial registration and alignment by minimizing the distance error between the two groups of point clouds. After the registration is completed, extract the characteristic parameters of the matched detection points, including position, debonding degree and the corresponding flame retardant residue rate value, and establish a detection point characteristic parameter data set under a unified coordinate system.

[0061] S5.2. Use the detection point characteristic parameter data set to train the coupled failure model, and calculate the failure coupling coefficient through a bivariate coupling equation. The expression is ; where Ψ is the failure coupling coefficient, R theory is the predicted residue rate of the flame retardant, and R actualis the actual residual rate of the flame retardant, S max is the maximum debonding area percentage, S critical is the critical debonding area, and λ is the coupling weight coefficient of the characteristics of the fireproof material.

[0062] Furthermore, for the data of reflection time delay, amplitude attenuation, and signal-to-noise ratio parameters, cleaning, denoising, and normalization processing are carried out. Combining the physical correlations among the reflection time delay, amplitude attenuation, and signal-to-noise ratio parameters, a failure model structure that can characterize the multi-factor coupling effect can be obtained. The characteristic parameter dataset of the detection points is divided into a training set and a validation set. Using the training set to input the characteristic parameters of each detection point, a supervised learning method is used to iteratively train the coupling failure model, continuously optimizing the model weights to minimize the failure discrimination error. The accuracy and generalization ability of the coupling failure model are evaluated on the validation set, and the performance is further improved by adjusting the hyperparameters of the coupling failure model, obtaining a coupling failure model that can accurately identify complex failure modes; the theoretical residual rate under different working conditions is obtained through finite element simulation, and the failure coupling coefficient is calculated by combining the measured data. The failure coupling coefficient comprehensively reflects the weighted influence of interface damage and flame retardant performance attenuation.

[0063] S5.3. Calculate the deviation between the actual residual rate of the flame retardant and the predicted residual rate of the flame retardant through the relative deviation analysis method. The expression is ; where △R is the deviation of the agent residual rate.

[0064] Furthermore, establish the theoretical residual rate curve under different temperature ranges as a benchmark, calculate the relative deviation between the actual value and the theoretical value of each detection point, consider the influence of measurement uncertainty during the analysis process, set the confidence interval to exclude the interference of random errors, and ensure the statistical significance of the deviation result.

[0065] S5.4. Use the weighted fusion method to combine the deviation of the flame retardant residual rate with the corrected phosphorus element spectral intensity to obtain the thermal degradation value.

[0066] Furthermore, the entropy weight-TOPSIS comprehensive evaluation method is used for the thermal degradation value calculation. The residual rate deviation and the nitrogen-phosphorus ratio abnormality are dimensionless processed, and the weights of each index are determined by combining subjective and objective weighting, obtaining a comprehensive evaluation value that reflects the thermal degradation degree of the material. The value quantifies the degree of thermal stability loss of the flame retardant system.

[0067] S5.5. When the failure coupling coefficient is less than the critical debonding area, it is determined as the interface debonding dominant failure.

[0068] Furthermore, the interface debonding-dominated failure determination is based on the principle of fracture mechanics. When the failure coupling coefficient value reflects the dominant influence of interface damage, it is determined as debonding-dominated failure. The degree of thermal degradation of the flame retardant is maintained within the inherent tolerance of the material. The failure mechanism is mainly manifested as the loss of structural integrity of the bonding interface, resulting in material delamination and peeling.

[0069] S5.6. When the failure coupling coefficient is greater than the thermal degradation value, it is determined as thermal degradation-dominated.

[0070] Furthermore, the thermal degradation-dominated failure determination is based on the theory of thermal analysis kinetics. When the failure coupling coefficient value clearly shows that the thermal degradation process plays a decisive role, even if the degree of interface damage is relatively low, it will fail due to the substantial reduction of flame retardant performance. In the failure mode, the macroscopic structure of the material remains intact, but the flame retardant function has seriously deteriorated.

[0071] S5.7. When the failure coupling coefficient is less than or equal to the critical debonding area and greater than or equal to the thermal degradation value, it is determined as composite failure type.

[0072] Furthermore, the composite failure type determination uses the fuzzy logic method and conducts a comprehensive evaluation by establishing a membership function of the failure mode. When the interface damage effect and the thermal degradation influence are in a critical degradation state, it is determined as composite failure, showing the synergistic deterioration characteristics of the mutual promotion of structural damage and functional degradation. The two failure mechanisms jointly lead to the accelerated deterioration of the material properties.

[0073] S6. Generate a three-dimensional visualization scheme according to the synergistic failure type of flame retardant attenuation and obtain a process optimization report.

[0074] S6.1. Based on the synergistic failure type, use the Kriging interpolation algorithm to construct a three-dimensional risk field. Through the Gaussian kernel function, obtain the thermal degradation area, interface debonding area, and composite failure area.

[0075] Furthermore, based on the synergistic failure type, identify the correlation characteristics between different failure modes, conduct an optimal unbiased estimation in space through the Kriging interpolation algorithm, construct a three-dimensional risk field, classify and calibrate the detection points, identify different failure modes such as thermal degradation, interface debonding, and composite failure, use the Kriging interpolation algorithm, take the characteristic parameters and failure types of the detection points as inputs, adjust the weights according to the spatial correlation between the detection points, realize the fine capture of the risk change trend, and divide the thermal degradation area, interface debonding area, and composite failure area by analyzing the eigenvalue distribution in different regions of the three-dimensional risk field, realizing the spatial visualization and precise positioning of the synergistic failure risk.

[0076] S6.2. Through the Pearson analysis of the three-dimensional risk field and the actual residual rate of the flame retardant, obtain the fire protection performance index, and the expression is ; where η is the fire protection performance index.

[0077] Furthermore, through the Pearson correlation analysis method, the relationship between the three-dimensional risk field and the actual residual rate of the flame retardant is evaluated, the dominant influence of the residual rate on the fire protection performance is clarified, and the calculation formula of the fire protection performance index is established: , in the expression, 0.7 and 0.3 are the residual rate weight coefficients, which reflect the proportion of the comprehensive contribution of the residual rate to the fire protection performance and ensure the accuracy of the fire protection performance index.

[0078] S6.3. When the fire protection performance index is greater than the thermal degradation zone, the interface debonding zone, and the composite failure zone at the same time, a three-dimensional visualization scheme is obtained.

[0079] Furthermore, the three-dimensional visualization scheme is generated using the VTK visualization toolkit, the risk field model and the fire protection performance index are spatially superimposed and displayed, the high-risk areas are automatically identified, and a comprehensive report including the material cross-section diagram, the failure distribution cloud diagram, and the performance index curve is generated.

[0080] S6.4. According to the three-dimensional visualization scheme, through the verification of the COMSOL multi-physics field simulation, a process optimization report is obtained.

[0081] Furthermore, the process optimization report is verified through the COMSOL Multiphysics multi-physics field coupling simulation, a finite element model including thermal-mechanical-chemical coupling is established, based on the problem areas identified in the visualization report, parameters such as the adhesive formula and the layout spacing of the anchor bolts are adjusted, and the effectiveness of the improvement scheme is verified through virtual experiments. A complete optimization scheme including material ratio, structural design, and construction technology is obtained, and the optimal process window is determined through the response surface analysis method.

[0082] This embodiment also provides an assembly type thermal insulation board fire protection performance detection system, including: A coordinate list module, which prepares an assembly type thermal insulation board sample, performs constant temperature and humidity balance and ultraviolet aging grouping treatment, obtains the interface of the standardized thermal insulation board sample, uses terahertz time-domain spectroscopy to scan the interface of the standardized thermal insulation board sample, and generates an interface debonding distribution diagram and a defect coordinate list; A residual rate module, which ablates the detection points of the assembly type thermal insulation board sample through laser-induced breakdown spectroscopy to obtain the plasma temperature, analyzes the flame retardant element migration data, and calculates the residual rate of the flame retardant; A failure type module, which inputs the interface debonding distribution diagram and the residual rate of the flame retardant into the coupling failure model to determine the collaborative failure type of interface debonding and flame retardant attenuation; An optimization scheme module, which generates a three-dimensional visualization scheme according to the collaborative failure type of flame retardant attenuation and obtains a process optimization report.

[0083] This embodiment also provides a computer device applicable to the case of the fire resistance performance detection method of the prefabricated thermal insulation board, 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 fire resistance performance detection method of the prefabricated thermal insulation board proposed in the above embodiment.

[0084] 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 a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented 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 covered on the display screen, or a button, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, a touchpad, or a mouse, etc.

[0085] This embodiment also provides a storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the fire resistance performance detection method of the prefabricated thermal insulation board 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 (abbreviated as SRAM), an electrically erasable programmable read-only memory (abbreviated as EEPROM), an erasable programmable read-only memory (abbreviated as EPROM), a programmable read-only memory (abbreviated as PROM), a read-only memory (abbreviated as ROM), a magnetic memory, a flash memory, a magnetic disk, or an optical disc.

[0086] In summary, through the collaborative detection of terahertz time-domain spectroscopy and laser-induced breakdown spectroscopy, the present invention realizes the multi-dimensional and precise evaluation of the fire performance of prefabricated insulation boards. Combining the ellipse fitting algorithm improves the detection accuracy of interface debonding defects. The laser-induced breakdown spectroscopy calculates the plasma temperature by the double spectral line intensity ratio method. Combining the Arrhenius equation and the dynamic nitrogen-phosphorus ratio correction model can effectively distinguish normal pyrolysis from abnormal migration. Through spatial matching, collaborative analysis is achieved. Based on the failure coupling coefficient formula, the chain failure mechanism between interface defects and flame retardant migration is quantitatively revealed for the first time. Through the process optimization report output by the three-dimensional risk field, the fire resistance of the insulation board in extreme environments is improved, breaking through the technical bottleneck that traditional detection techniques cannot synchronously analyze interface structures and element migrations, providing an integrated solution for prefabricated buildings from microscopic defect identification to macroscopic performance prediction.

[0087] 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 fire resistance performance of prefabricated insulation boards, characterized in that: including Prepare prefabricated thermal insulation board samples, conduct constant temperature and humidity equilibration and ultraviolet aging grouping treatments to obtain the interfaces of standardized thermal insulation board samples, use terahertz time-domain spectroscopy to scan the interfaces of standardized thermal insulation board samples, and generate interface debonding distribution maps and defect coordinate lists; Ablate the detection points of the prefabricated thermal insulation board samples by laser-induced breakdown spectroscopy to obtain the plasma temperature, analyze the migration data of flame retardant elements, and calculate the flame retardant residue rate; Input the interface debonding distribution map and the flame retardant residue rate into the coupled failure model to determine the type of synergistic failure of interface debonding and flame retardant attenuation; Generate a three-dimensional visualization scheme according to the type of synergistic failure of flame retardant attenuation and obtain a process optimization report.

2. The fire resistance performance testing method of the prefabricated thermal insulation board according to claim 1, characterized in that: Prepare prefabricated thermal insulation board samples, conduct constant temperature and humidity equilibration and ultraviolet aging grouping treatments to obtain the interfaces of standardized thermal insulation board samples, including the following steps Prepare prefabricated thermal insulation board samples using the factory standard process; Place the prefabricated thermal insulation board samples in a constant temperature and humidity chamber for equilibration treatment to obtain prefabricated thermal insulation board samples after internal stress elimination and equilibration; Divide the prefabricated thermal insulation board samples after internal stress elimination and equilibration into an experimental group and a control group; Use a QUV accelerated aging instrument to continuously irradiate the experimental group and keep the control group away from light to obtain the interfaces of standardized thermal insulation board samples.

3. The fire resistance performance detection method of the assembled thermal insulation board according to claim 2, wherein: Use terahertz time-domain spectroscopy to scan the interfaces of standardized thermal insulation board samples and generate interface debonding distribution maps and defect coordinate lists, including the following steps Use transmission terahertz time-domain spectroscopy to perform a planar grid scan on the interfaces of standardized thermal insulation board samples to obtain the time-domain reflection signals of each detection point, and extract signal characteristic parameters through signal processing algorithms; Use wavelet transform time-domain analysis method to extract reflection delay, amplitude attenuation, and signal-to-noise ratio from the signal characteristic parameters, and set reflection delay threshold, amplitude attenuation threshold, and signal-to-noise ratio threshold; When the reflection delay, amplitude attenuation, and signal-to-noise ratio are all greater than the reflection delay threshold, amplitude attenuation threshold, and signal-to-noise ratio threshold, obtain the defect area; Use the ellipse fitting algorithm to calculate the center coordinates and equivalent diameter of the defect area, and generate interface debonding distribution maps and defect coordinate lists.

4. The fire resistance performance detection method of the prefabricated thermal insulation board according to claim 3, characterized in that: Ablate the detection points of the prefabricated thermal insulation board samples by laser-induced breakdown spectroscopy to obtain the plasma temperature including the following steps Use laser-induced breakdown spectroscopy to ablate the detection points of the prefabricated thermal insulation board to obtain the original spectral data, and obtain the intensity ratio of the characteristic spectral lines of flame retardant elements from the original spectral data through peak fitting algorithms; Use the double spectral line intensity ratio method to calculate the plasma temperature of the intensity ratio of the characteristic spectral lines of flame retardant elements.

5. The fire resistance performance detection method of the prefabricated thermal insulation board according to claim 4, characterized in that: Analyze the migration data of flame retardant elements and calculate the flame retardant residue rate including the following steps Based on the plasma temperature, establish a temperature-decomposition relationship model using the Arrhenius equation, input the plasma temperature into the temperature-decomposition relationship model, and calculate the thermal decomposition ratio of the flame retardant; According to the thermal decomposition ratio of the flame retardant, calibrate the intensity of the characteristic spectral lines of phosphorus elements through a dynamic correction formula; Correct the intensity of the phosphorus element spectral lines according to the thermal decomposition ratio of the flame retardant; Compare the intensity of the phosphorus element spectral lines after correction with the standard intensity of the phosphorus element to obtain the actual flame retardant residue rate.

6. The fire resistance performance detection method of the prefabricated thermal insulation board according to claim 5, characterized in that: Input the interface debonding distribution map and the flame retardant residue rate into the coupling failure model to determine the type of synergistic failure between interface debonding and flame retardant attenuation, including the following steps, Use the ICP point cloud registration algorithm to perform spatial matching on the interface debonding distribution map and the flame retardant residue rate, and establish a dataset of characteristic parameters of detection points; Use the dataset of characteristic parameters of detection points to train the coupling failure model, and calculate the failure coupling coefficient through a bivariate coupling equation; Calculate the deviation between the actual residue rate of the flame retardant and the predicted residue rate of the flame retardant through the relative deviation analysis method; Use the weighted fusion method to combine the deviation of the flame retardant residue rate with the corrected phosphorus element spectral intensity to obtain the thermal degradation value; When the failure coupling coefficient is less than the critical debonding area, it is determined as an interface debonding-dominated failure; When the failure coupling coefficient is greater than the thermal degradation value, it is determined as a thermal degradation-dominated type; When the failure coupling coefficient is less than or equal to the critical debonding area and greater than or equal to the thermal degradation value, it is determined as a composite failure type.

7. The fire resistance performance detection method of the assembled insulation board according to claim 6, characterized in that: Generate a three-dimensional visualization scheme according to the type of synergistic failure of flame retardant attenuation, and obtain a process optimization report, including the following steps, Based on the type of synergistic failure, use the Kriging interpolation algorithm to construct a three-dimensional risk field, and obtain the thermal degradation area, interface debonding area, and composite failure area through the Gaussian kernel function; Through the Pearson analysis of the three-dimensional risk field and the actual residue rate of the flame retardant, obtain the fire protection performance index; When the fire protection performance index is greater than the thermal degradation area, interface debonding area, and composite failure area at the same time, obtain a three-dimensional visualization scheme; According to the three-dimensional visualization scheme, verify through COMSOL multi-physics field simulation to obtain a process optimization report.

8. An assembled thermal insulation board fire performance detection system, based on the assembled thermal insulation board fire performance detection method according to any one of claims 1 to 7, characterized in that: Including, The coordinate list module prepares a prefabricated thermal insulation board sample, performs constant temperature and humidity equilibrium and ultraviolet aging grouping treatment, obtains the interface of the standardized thermal insulation board sample, uses terahertz time-domain spectroscopy to scan the interface of the standardized thermal insulation board sample, and generates an interface debonding distribution map and a defect coordinate list; The residue rate module ablates the detection points of the prefabricated thermal insulation board sample through laser-induced breakdown spectroscopy to obtain the plasma temperature, analyzes the flame retardant element migration data, and calculates the flame retardant residue rate; The failure type module inputs the interface debonding distribution map and the flame retardant residue rate into the coupling failure model to determine the type of synergistic failure between interface debonding and flame retardant attenuation; The optimization scheme module generates a three-dimensional visualization scheme according to the type of synergistic failure of flame retardant attenuation, and obtains a process optimization report.

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, it implements the steps of the fire protection performance detection method for the prefabricated thermal insulation board according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the fire protection performance detection method for the prefabricated thermal insulation board according to any one of claims 1 to 7.

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