Novel rapid detection method of thermal insulation materials based on spectral analysis

Abstract

The present application relates to the technical field of spectral analysis, in particular to a novel rapid detection method for thermal insulation materials based on spectral analysis, which solves the technical problem of low detection accuracy in the prior art. The method comprises: performing multiple infrared spectrum scans on each detection point of the novel thermal insulation material on the construction site respectively, and collecting multiple infrared spectrum images corresponding to each detection point; the infrared spectrum images are used to represent the absorption intensity of the novel thermal insulation material at different wave numbers; performing environmental interference analysis on the multiple infrared spectrum images corresponding to each detection point to determine adaptive filtering parameters; the adaptive filtering parameters are used to represent the severity of environmental noise interference on the construction site; based on the adaptive filtering parameters, the multiple infrared spectrum images corresponding to each detection point are filtered to obtain filtered infrared spectrum images; and the detection result of the novel thermal insulation material is obtained according to the filtered infrared spectrum images.

Description

Technical Field

[0001] This invention relates to the field of spectral analysis technology, and specifically to a novel rapid detection method for thermal insulation materials based on spectral analysis. Background Technology

[0002] With the widespread application of new thermal insulation materials in building engineering, industrial equipment insulation, and other fields, the demand for rapid testing of their composition and quality is becoming increasingly urgent. Spectroscopic analysis-based testing technology, with its advantages of speed, non-destructive testing, and portability, has become the mainstream technology for testing new thermal insulation materials. Its core principle is to utilize the correlation between the composition, structure, and physical properties of thermal insulation materials and their spectral characteristics. By capturing the "spectral fingerprint" formed by the material's absorption, reflection, or transmission response to light in specific wavelengths, it enables the evaluation of key material indicators, making it suitable for various scenarios such as manufacturing, engineering applications, and operation and maintenance monitoring.

[0003] In engineering field testing scenarios, various complex environments are often encountered. Interference factors such as temperature and humidity fluctuations, dust, water vapor, and volatile organic compounds can introduce additional absorption peaks or spectral overlap into the acquired infrared spectra, creating noise interference. This, in turn, affects the accuracy of characteristic peak identification, ultimately leading to significant deviations in the test results. Consequently, it is difficult to meet the high accuracy requirements of engineering field testing, limiting the reliable application of this type of testing technology in practical engineering scenarios. Summary of the Invention

[0004] To address the problem of low accuracy in existing detection technologies, the present invention aims to provide a novel rapid detection method for thermal insulation materials based on spectral analysis. The specific technical solution adopted is as follows:

[0005] This application provides a novel rapid detection method for thermal insulation materials based on spectral analysis, including:

[0006] Multiple infrared spectral scans were performed on each test point of the new thermal insulation material at the engineering site, and multiple infrared spectral images corresponding to each test point were collected. The infrared spectral images are used to characterize the absorption intensity of the new thermal insulation material at different wavenumbers.

[0007] Environmental interference analysis was performed based on multiple infrared spectra corresponding to each detection point to determine adaptive filtering parameters; the adaptive filtering parameters are used to characterize the severity of environmental noise interference at the engineering site.

[0008] Based on adaptive filtering parameters, multiple infrared spectra corresponding to each detection point are filtered to obtain the filtered infrared spectra.

[0009] The test results of the new thermal insulation material are obtained based on the filtered infrared spectrum.

[0010] In one possible implementation, the method includes:

[0011] The overall dynamic fluctuation coefficient is determined by analyzing the fluctuation of the spectral signal of the new thermal insulation material over time based on multiple infrared spectra corresponding to each detection point.

[0012] Spatial difference analysis was conducted on the engineering site based on multiple infrared spectra corresponding to each detection point to determine the spatial heterogeneity coefficient. The spatial heterogeneity coefficient is used to characterize the degree of interference difference between spectral signals of different detection points on the new thermal insulation material.

[0013] The adaptive filtering parameters are determined based on the overall dynamic volatility coefficient and the spatial heterogeneity coefficient.

[0014] In one possible implementation, the method includes:

[0015] For each detection point, a fluctuation feature sequence is generated based on multiple infrared spectra corresponding to the detection point; the fluctuation feature sequence is used to characterize the degree of fluctuation in absorption intensity at each wavenumber corresponding to the detection point.

[0016] Determine at least one target subsequence based on the fluctuation characteristic sequence;

[0017] The local dynamic fluctuation coefficient of the detection point is determined based on the absorption intensity at each wavenumber in at least one target subsequence.

[0018] The overall dynamic fluctuation coefficient is determined based on the local dynamic fluctuation coefficients of each detection point.

[0019] In one possible implementation, the method includes:

[0020] For each detection point, the absorption intensity of each wavenumber in different infrared spectra is obtained from multiple infrared spectra corresponding to the detection point.

[0021] For each wavenumber, the absorption intensity fluctuation value corresponding to the wavenumber is determined based on the absorption intensity in different infrared spectra.

[0022] The fluctuation characteristic sequence of the detection point is determined based on the absorption intensity fluctuation value corresponding to each wavenumber; the fluctuation characteristic sequence includes the absorption intensity fluctuation values ​​corresponding to each wavenumber in ascending order.

[0023] In one possible implementation, the method includes:

[0024] The screening threshold is determined based on the absorption intensity fluctuation value corresponding to each wave number in the fluctuation feature sequence, and target absorption intensity fluctuation values ​​greater than the screening threshold are selected from the fluctuation feature sequence.

[0025] By merging adjacent target absorption intensity fluctuation values ​​in the fluctuation feature sequence, at least one target subsequence is obtained.

[0026] In one possible implementation, the method includes:

[0027] For each detection point, the target infrared spectrum is selected from multiple infrared spectrum images corresponding to the detection point;

[0028] For any two detection points, the target infrared spectra of the two detection points are compared and analyzed to determine the difference in absorption intensity of the two detection points at each wavenumber.

[0029] The spatial heterogeneity coefficient is determined based on the difference in absorption intensity between any two detection points at various wavenumbers.

[0030] In one possible implementation, the method includes:

[0031] For any two detection points, the set of interfering wavenumbers and the interference difference value corresponding to the two detection points are determined based on the difference in absorption intensity of the two detection points at each wavenumber. The set of interfering wavenumbers consists of wavenumbers whose absorption intensity difference value is greater than a preset difference threshold. The interference difference value is used to characterize the overall degree of difference in absorption intensity between the two detection points.

[0032] The spatial heterogeneity coefficient is determined based on the set of interference wavenumbers corresponding to any two detection points and the interference difference value.

[0033] In one possible implementation, the method includes:

[0034] The filter window length is determined based on the adaptive filter parameters;

[0035] Based on the filter window length, a sliding window filter is applied to multiple infrared spectra corresponding to each detection point to obtain the filtered infrared spectra.

[0036] In one possible implementation, the method includes:

[0037] For each infrared spectrum, identify the characteristic peak regions and non-characteristic peak regions in the infrared spectrum;

[0038] Based on the filter window length, the local adaptive filter window length corresponding to the characteristic peak region and the local adaptive filter window length corresponding to the non-characteristic peak region in the infrared spectrum are determined respectively.

[0039] Sliding window filtering is performed on the characteristic peak regions based on the local adaptive filtering window length corresponding to the characteristic peak regions in the infrared spectrum, and sliding window filtering is performed on the non-characteristic peak regions based on the local adaptive filtering window length corresponding to the non-characteristic peak regions, to obtain the filtered infrared spectrum.

[0040] In one possible implementation, the method includes:

[0041] Extract multiple characteristic peak spectra from the filtered infrared spectrum;

[0042] The test results of the new thermal insulation material were determined by matching and analyzing the spectra of multiple characteristic peaks with the standard spectra of each material.

[0043] The present invention has the following beneficial effects:

[0044] To address the issue of low accuracy in existing detection technologies, this application provides a novel rapid detection method for thermal insulation materials based on spectral analysis. By performing multiple infrared spectral scans on various detection points of the novel thermal insulation material at the engineering site, multiple infrared spectra corresponding to each detection point are acquired. This multi-point detection and multiple scans ensure the reliability and representativeness of the spectral data, providing a high-quality data foundation for subsequent analysis. Subsequently, environmental interference analysis is performed based on the multiple infrared spectra corresponding to each detection point to determine adaptive filtering parameters. Based on these parameters, the multiple infrared spectra corresponding to each detection point are filtered to obtain filtered infrared spectra. This achieves the strategy of dynamically adjusting filtering parameters according to environmental interference, enabling adaptive filtering. Finally, the detection results of the novel thermal insulation material are obtained based on the filtered infrared spectra, ensuring the objectivity and accuracy of the detection results. This solution specifically addresses the problem of insufficient detection accuracy caused by environmental noise interference at engineering sites, providing a reliable solution for rapid detection in engineering projects. Attached Figure Description

[0045] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 This is one of the flowcharts illustrating a novel rapid detection method for thermal insulation materials based on spectral analysis, provided in an embodiment of the present invention.

[0047] Figure 2This is a second schematic flowchart of a novel rapid detection method for thermal insulation materials based on spectral analysis, provided in one embodiment of the present invention.

[0048] Figure 3 This is a third schematic flowchart of a novel rapid detection method for thermal insulation materials based on spectral analysis, provided as an embodiment of the present invention.

[0049] Figure 4 This is a fourth flowchart illustrating a novel rapid detection method for thermal insulation materials based on spectral analysis, provided as an embodiment of the present invention.

[0050] Figure 5 The fifth flowchart illustrates a novel rapid detection method for thermal insulation materials based on spectral analysis, provided as an embodiment of the present invention. Detailed Implementation

[0051] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of the novel rapid detection method for thermal insulation materials based on spectral analysis proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0052] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0053] In all division and logarithmic operations covered in this application, a smoothing mechanism is employed to prevent computer program crashes or invalid values ​​from being generated due to a zero denominator or a zero input. Specifically, a positive correction factor is superimposed on the denominator term of the division operation or the argument term of the logarithmic function. For example, the value is This ensures the robustness and feasibility of the algorithm under extreme conditions.

[0054] The normalization function mentioned in this application Unless otherwise specified, all values ​​are normalized using maximum and minimum values. The maximum and minimum values ​​are preset empirical extreme values ​​derived from a large amount of historical experimental data. If the calculated result exceeds the [0,1] interval, it is restricted to the [0,1] range by a truncation function (i.e., if the result is less than 0, it is taken as 0, and if it is greater than 1, it is taken as 1) to eliminate the influence of outliers on the evaluation index.

[0055] To address the issue of low accuracy in existing detection technologies, this application provides a novel rapid detection method for thermal insulation materials based on spectral analysis. By performing multiple infrared spectral scans on various detection points of the novel thermal insulation material at the engineering site, multiple infrared spectra corresponding to each detection point are acquired. This multi-point detection and multiple scans ensure the reliability and representativeness of the spectral data, providing a high-quality data foundation for subsequent analysis. Subsequently, environmental interference analysis is performed based on the multiple infrared spectra corresponding to each detection point to determine adaptive filtering parameters. Based on these parameters, the multiple infrared spectra corresponding to each detection point are filtered to obtain filtered infrared spectra. This achieves the strategy of dynamically adjusting filtering parameters according to environmental interference, enabling adaptive filtering. Finally, the detection results of the novel thermal insulation material are obtained based on the filtered infrared spectra, ensuring the objectivity and accuracy of the detection results. This solution specifically addresses the problem of insufficient detection accuracy caused by environmental noise interference at engineering sites, providing a reliable solution for rapid detection in engineering projects.

[0056] The specific scheme of the novel rapid detection method for thermal insulation materials based on spectral analysis provided by the present invention will be described in detail below with reference to the accompanying drawings.

[0057] Please see Figure 1 The diagram illustrates a flowchart of a novel rapid detection method for thermal insulation materials based on spectral analysis, according to an embodiment of the present invention. The method includes the following steps:

[0058] Step 101: Perform multiple infrared spectral scans on each test point on the new thermal insulation material at the engineering site, and collect multiple infrared spectral images corresponding to each test point.

[0059] Infrared spectroscopy is used to characterize the absorption intensity of novel thermal insulation materials at different wavenumbers. For example, infrared spectra are typically plotted with wavenumber as the horizontal axis (units are usually cm). -1 The vertical axis represents absorption intensity, reflecting the material's absorption characteristics for infrared light of different wavelengths. The novel thermal insulation material in this application can be a commonly used type of engineering thermal insulation material, such as siloxane aerogel, carbon composite porous powder, polyurethane composite thermal insulation material, etc.

[0060] In some embodiments, the selection of test points must meet representativeness requirements, avoiding easily interfered locations such as edges, defects, and joints. For example, 3-5 test points can be evenly selected on the surface of the new thermal insulation material, with a spacing of no less than 5cm between each test point to ensure comprehensive test coverage. Before collection, the surface of the test area needs to be pretreated. For example, it can be wiped with a lint-free cloth soaked in anhydrous ethanol or deionized water to remove visible contaminants such as stains, dust, and cement residue, reducing interference from external impurities on the spectral signal.

[0061] For example, this application can use a portable Fourier transform infrared spectrometer for scanning, paired with a detector adapted to novel thermal insulation materials (such as a mercury cadmium telluride (MCT) detector adapted to the mid-infrared band) to improve detection sensitivity. Before scanning, instrument preparation must be completed, including cleaning the probe window to prevent obstruction by dirt, checking fiber optic connections to ensure signal stability, and allowing the instrument to warm up for 30 minutes to stabilize the light source and detector, while simultaneously performing baseline and wavelength calibration. Each detection point is scanned multiple times (e.g., 3 times) to acquire multiple infrared spectra, providing sufficient data support for subsequent environmental interference analysis.

[0062] Step 102: Analyze environmental interference based on multiple infrared spectra corresponding to each detection point and determine the adaptive filtering parameters.

[0063] Among them, the adaptive filtering parameter is used to characterize the severity of environmental noise interference at the engineering site, reflecting the strength of interference of the engineering site environment on the spectral signal. The larger the value, the higher the severity of noise interference.

[0064] This application can indirectly quantify the noise impact caused by environmental factors (such as temperature and humidity fluctuations, gas interference, local pollution differences, etc.) by analyzing the fluctuation patterns and differences in absorption intensity in multiple infrared spectra corresponding to each detection point, thereby avoiding the bias in the selection of filter parameters caused by subjective judgment.

[0065] In some embodiments, this application can combine the spectral repeatability of multiple scans at the same detection point and the spectral differences between different detection points, and convert the degree of interference into quantifiable parameter values ​​through statistical analysis, feature extraction and other methods, so as to ensure that the adaptive filtering parameters can objectively and comprehensively reflect the on-site interference status.

[0066] Step 103: Based on the adaptive filtering parameters, filter the multiple infrared spectra corresponding to each detection point to obtain the filtered infrared spectra.

[0067] By employing filtering, this application can remove interference signals caused by environmental noise while preserving the characteristic spectral information of the thermal insulation material itself. For example, this application can use a sliding window filtering algorithm, which smooths the data within the window by sliding a fixed-length window across the spectral data, thereby achieving noise suppression.

[0068] For example, this application can dynamically adjust the window length according to the size of the adaptive filtering parameters. When the interference is severe, a larger window is selected to enhance the denoising effect, and when the interference is small, a smaller window is selected to retain detailed features, ensuring the targeting and effectiveness of the filtering process and avoiding the problems of excessive denoising and loss of details or insufficient denoising and residual interference caused by fixed window filtering.

[0069] Step 104: Obtain the test results of the new thermal insulation material based on the filtered infrared spectrum.

[0070] The test results mainly include qualitative judgment of material composition, that is, confirming whether the material base material and main additives are consistent with the nominal values, and whether there are any abnormalities such as the addition of inferior fillers.

[0071] In one possible implementation, this application can extract multiple characteristic peak spectra from the filtered infrared spectrum, and then match and analyze the multiple characteristic peak spectra with the standard spectra of each material to determine the test results of the new thermal insulation material.

[0072] For example, this application can perform baseline correction on the filtered infrared spectrum to eliminate baseline shifts caused by instrument drift, background noise, etc., ensuring the accuracy of absorption intensity. Then, the absorption peak spectra in the infrared spectrum are identified. An absorption peak spectrum is defined as one where the absorption intensity is greater than 1.2 times the absorption intensity of adjacent wavenumber points, and the half-width at half-maximum (FWHM) does not exceed 20 cm. -1 The spectral range is such that it can effectively distinguish between true characteristic peak spectra and weak noise peak spectra.

[0073] This application can sort the identified absorption peak spectra from largest to smallest absorption intensity, and select the top 3-5 absorption peak spectra as characteristic peak spectra to ensure that the extracted characteristic peak spectra are representative (strong peaks are less affected by noise and can reflect the main functional groups of the material).

[0074] Subsequently, matching analysis was performed using industry-standard spectral libraries or pre-established standard spectral databases containing various nominal new thermal insulation materials and their common additives and inferior fillers. The matching process included:

[0075] The wavenumber of each extracted characteristic peak spectrum is compared with the wavenumbers of each characteristic peak in a standard spectrum in the database. The characteristic peak with the closest wavenumber is selected as the comparison target, and its absolute deviation is calculated. If the wavenumber deviations of all compared characteristic peaks are less than a preset wavenumber tolerance threshold (e.g., ±5 cm⁻¹), the comparison is complete. -1 If the characteristic peak is exactly matched with the standard spectrum at the wavenumber position, then it is considered that the characteristic peak has successfully matched the standard spectrum.

[0076] For a standard spectrum, if more than a preset proportion (e.g., 80%) of the extracted characteristic peaks are successfully matched at the wavenumber position, and the relative intensity relationship of these successfully matched characteristic peaks is basically consistent with that of the standard spectrum (e.g., the order of the strongest peak and the second strongest peak is the same, and the intensity ratio is within a preset range), then it is determined that the thermal insulation material being tested is successfully matched with the standard spectrum.

[0077] If the material model, substrate, and main additive type corresponding to the successfully matched standard spectrum are completely consistent with the nominal information of the material to be tested, then the test result is that the material composition is consistent with the nominal information.

[0078] In addition, this application can also detect and analyze the consistency of additives and the presence of inferior fillers.

[0079] For additive consistency analysis, the determination is made by checking whether the successfully matched characteristic peaks include the characteristic absorption peaks unique to the nominal additive (e.g., the characteristic peak of a certain flame retardant at a specific wavenumber). If the key characteristic peaks of the nominal additive appear and match well, the additive is determined to be consistent with the nominal additive; if they are missing, it is determined to be inconsistent.

[0080] For the analysis of doping with inferior fillers, if during the matching process, significant unknown peaks are found in the extracted characteristic peak spectra that cannot match any standard spectra of the nominal material and additives, or peaks that highly match the characteristic peaks of common inferior fillers (such as overfilled calcium carbonate, wollastonite, etc.), it suggests that there may be an abnormality of doping with inferior fillers. This can be confirmed by performing a secondary matching between the unknown peaks and the standard spectral library of inferior fillers.

[0081] If no matching standard spectrum is found, the test result indicates that the material composition does not match the nominal composition. The report should list specific abnormal characteristics, such as: the absence of a key characteristic peak of the nominal substrate; the appearance of an unknown characteristic peak; the failure to detect characteristic peaks of the nominal additives or abnormal intensity, etc.

[0082] In some embodiments, to further improve the reliability of the test results, the present application may also perform the above technical solutions multiple times, and may also perform on-site synchronous testing on standard samples, thereby further verifying the reliability of the test and ensuring the stability and representativeness of the test results.

[0083] Based on the above technical solution, this application performs multiple infrared spectral scans on various detection points of the new thermal insulation material at the engineering site, acquiring multiple infrared spectra corresponding to each detection point. This achieves multi-point detection and multiple scans, ensuring the reliability and representativeness of the spectral data and providing a high-quality data foundation for subsequent analysis. Subsequently, environmental interference analysis is performed based on the multiple infrared spectra corresponding to each detection point to determine adaptive filtering parameters. Based on these parameters, the multiple infrared spectra corresponding to each detection point are filtered to obtain filtered infrared spectra. This achieves the strategy of dynamically adjusting filtering parameters according to environmental interference, enabling adaptive filtering. Finally, the detection results of the new thermal insulation material are obtained based on the filtered infrared spectra, ensuring the objectivity and accuracy of the detection results. This solution specifically addresses the problem of insufficient detection accuracy caused by environmental noise interference at engineering sites, providing a reliable solution for rapid detection at engineering sites.

[0084] As one possible embodiment of this application, combined with Figure 1 ,like Figure 2 As shown, step 102 above can be achieved through the following steps:

[0085] Step 201: Perform a fluctuation analysis on the engineering site based on multiple infrared spectra corresponding to each detection point to determine the overall dynamic fluctuation coefficient.

[0086] The overall dynamic fluctuation coefficient is used to characterize the degree of interference fluctuation in the spectral signal of the new thermal insulation material over time. The overall dynamic fluctuation coefficient can reflect the influence of changes in the engineering site environment over time (such as temperature and humidity fluctuations, airflow disturbances, changes in gas composition, etc.) on the spectral signal. In the spectral data of multiple scans at the same detection point, the greater the fluctuation in absorption intensity, the more serious the interference in the time dimension, and the larger the overall dynamic fluctuation coefficient.

[0087] It should be noted that since the engineering site is not a stable laboratory environment, even slight fluctuations in temperature, humidity, airflow, and gas composition can directly affect the transmission of infrared light and the surface condition of the sample. For example, airflow fluctuations, heat dissipation from nearby equipment, and air movement caused by personnel movement at the engineering site can all cause short-term fluctuations in temperature and humidity in the local area of ​​the detection point, leading to spectral differences. In addition, airflow disturbances can also cause changes in the content of gas molecules in the spectrometer's optical path (the gap between the probe and the material surface), affecting the characteristic absorption peaks of these gases (e.g., CO2 at 2349 cm⁻¹). -1 The intensity difference will appear in repeated scans, which will affect the overall spectrum, resulting in differences in the infrared spectrum of the same detection point in repeated scans.

[0088] Since the material's composition and structure remain unchanged when the same detection point is repeatedly scanned within a short period, the fluctuations in the spectral signal are mainly caused by environmental factors. Therefore, this application can quantify these fluctuations to reflect the degree of interference from environmental fluctuations over time. For example, the degree of fluctuation can be quantified by calculating the variance of the absorption intensity at the same wavenumber and the characteristics of abnormal fluctuation segments in different scan spectra of the same detection point. Finally, the overall dynamic volatility coefficient is obtained based on the fluctuation characteristics of all detection points.

[0089] Step 202: Based on multiple infrared spectra corresponding to each detection point, perform spatial difference analysis on the engineering site to determine the spatial heterogeneity coefficient.

[0090] The spatial heterogeneity coefficient is used to characterize the degree of interference difference in spectral signals between different test points on a novel thermal insulation material. The spatial heterogeneity coefficient reflects the differences in environmental interference at different locations on the engineering site (such as dust adsorption, surface contamination levels, and local gas concentrations at different test points). The greater the spectral difference between different test points, the more uneven the spatial interference, and the larger the spatial heterogeneity coefficient.

[0091] It should be noted that the above steps analyzed the dynamic fluctuations of the engineering site environment at the same detection point over time, i.e., the changes in the gap between the probe and the material surface over time. However, even in the same area, the concentration of interference sources, adsorption state, and contact conditions at different detection points will vary, ultimately leading to heterogeneity in the intensity and type of spectral interference. Therefore, this application can further analyze the differences between infrared spectra at different detection points (such as differences in the adsorption of impurities on the material surface at different detection points) to determine the spatial heterogeneity of the engineering site environment within the detection area.

[0092] The composition and structure of the same thermal insulation material are spatially consistent. Excluding the possibility of material inhomogeneity, the spectral differences between different testing points are mainly caused by local environmental interference. Therefore, this application can quantify these differences to reflect the degree of spatial interference. For example, by comparing the characteristic spectra of different testing points, the differences in absorption intensity, the distribution of interference wavenumbers, etc., can be calculated to quantify spatial differences and obtain the spatial heterogeneity coefficient.

[0093] Step 203: Determine the adaptive filtering parameters based on the overall dynamic volatility coefficient and the spatial heterogeneity coefficient.

[0094] For example, the adaptive filtering parameters satisfy the following formula:

[0095]

[0096] in, For adaptive filtering parameters, This represents the overall dynamic volatility coefficient. is the spatial heterogeneity coefficient. The weighting coefficient, ranging from 0 to 1, is used to adjust the influence of the overall dynamic volatility coefficient and the spatial heterogeneity coefficient, and can be determined based on experimental data statistics.

[0097] Optionally, if the main disturbance source at the engineering site is dynamically changing (such as time-dimensional fluctuations caused by strong winds or gas flow), then The value should be greater than 0.5; if the main source of interference is spatially uneven (such as local dust coverage, uneven coating thickness), then The value should be less than 0.5.

[0098] Among them, the adaptive filtering parameters take into account both the fluctuation interference in the time dimension and the difference interference in the spatial dimension, ensuring a comprehensive characterization of the interference in the engineering site environment.

[0099] Based on the above technical solution, this application can refine environmental interference analysis into fluctuation analysis and spatial difference analysis, quantifying environmental interference from both temporal and spatial dimensions. Fluctuation analysis is performed on the engineering site using multiple infrared spectra corresponding to each detection point to determine the overall dynamic fluctuation coefficient, thereby achieving environmental fluctuation interference analysis in the temporal dimension to reflect dynamic environmental changes. Spatial difference analysis is then performed on the engineering site using multiple infrared spectra corresponding to each detection point to determine the spatial heterogeneity coefficient, thereby achieving local interference differences in the spatial dimension to reflect regional interference differences. Finally, adaptive filtering parameters are determined based on the overall dynamic fluctuation coefficient and the spatial heterogeneity coefficient, making interference quantification more comprehensive and accurate, further improving the reliability of the adaptive filtering parameters, providing a more scientific basis for subsequent filtering processing, and thus further improving the accuracy of the detection results.

[0100] As one possible embodiment of this application, combined with Figure 2 ,like Figure 3 As shown, step 201 above can be achieved through the following steps:

[0101] Step 301: For each detection point, generate a fluctuation feature sequence of the detection point based on multiple infrared spectra corresponding to the detection point.

[0102] Among them, the fluctuation feature sequence is used to characterize the degree of fluctuation of absorption intensity at each wavenumber corresponding to the detection point.

[0103] In one possible implementation, this application can obtain the absorption intensity of each wavenumber in different infrared spectra in multiple infrared spectra corresponding to each detection point.

[0104] For example, taking the case where each detection point undergoes three infrared spectral scans, each detection point corresponds to three infrared spectra, with wavenumbers ranging from 400 to 4000 cm⁻¹. -1 Each wavenumber detected at this detection point corresponds to the absorption intensity value in three infrared spectra.

[0105] Then, for each wavenumber, the absorption intensity fluctuation value corresponding to the wavenumber is determined based on the absorption intensity in different infrared spectra.

[0106] For example, this application can use the variance of the absorption intensity in different infrared spectra of the wavenumber as the absorption intensity fluctuation value corresponding to the wavenumber, thereby effectively reflecting the dispersion of the data and avoiding the problem of positive and negative deviations canceling each other out.

[0107] Thus, this application can determine the fluctuation characteristic sequence of the detection point based on the absorption intensity fluctuation value corresponding to each wavenumber.

[0108] The fluctuation feature sequence includes the absorption intensity fluctuation values ​​corresponding to each wavenumber, sorted in ascending order. The sequence length is consistent with the wavenumber range (i.e., the length of the fluctuation feature sequence is the total number of wavenumbers), ensuring the orderliness and completeness of the fluctuation features.

[0109] Step 302: Determine at least one target subsequence based on the fluctuation characteristic sequence.

[0110] Among them, the target subsequence is a continuous segment in the wave characteristic sequence with a significantly higher absorption intensity fluctuation value, which indicates that there is strong environmental wave interference within this wavenumber range.

[0111] In one possible implementation, this application can determine the screening threshold based on the absorption intensity fluctuation value corresponding to each wave number of the fluctuation feature sequence, and screen out the target absorption intensity fluctuation value that is greater than the screening threshold from the fluctuation feature sequence. Then, the target absorption intensity fluctuation values ​​that are adjacent in the fluctuation feature sequence are merged to obtain at least one target subsequence.

[0112] For example, the screening threshold can be determined based on the mean and standard deviation of all absorption intensity fluctuations. For instance, the difference between the mean and the standard deviation can be used as the screening threshold to effectively identify outliers that significantly deviate from normal fluctuation levels, while taking into account the overall distribution characteristics of the data.

[0113] This application can traverse the wave characteristic sequence and merge the target absorption intensity wave values ​​that are adjacent to each other according to the corresponding wave number into a continuous segment. Each continuous segment is a target subsequence, and a single isolated target absorption intensity wave value also constitutes a target subsequence (with a length of 1), ensuring that no local strong interference region is missed.

[0114] Step 303: Determine the local dynamic fluctuation coefficient of the detection point based on the absorption intensity at each wavenumber in at least one target subsequence.

[0115] It should be noted that if no target subsequence is found, the local dynamic volatility coefficient can be set to 0 directly.

[0116] In one possible implementation, this application can calculate the environmental fluctuation impact coefficient for each target subsequence.

[0117] For example, the coefficient of influence of environmental fluctuations satisfies the following formula:

[0118]

[0119] in, For the first Environmental fluctuation impact coefficient of each target subsequence For the first The sequence length of a target subsequence (i.e., the number of wavenumbers contained in the target subsequence). The sum of the sequence lengths of all target subsequences. For the first The mean of the target absorption intensity fluctuation values ​​in each target subsequence For the first The maximum value of the target absorption intensity fluctuation value in each target subsequence.

[0120] This indicates the proportion of the target subsequence's length among all target subsequences; a larger proportion indicates that the target subsequence covers a wider range of wavenumbers. The average of the mean and maximum values ​​of the target subsequence, which characterizes the fluctuation intensity level, can take into account both the overall level and extreme cases, avoiding the one-sidedness of a single indicator. The larger the proportion of the target subsequence in length and the higher the fluctuation intensity, the more severe its interference with the spectral signal.

[0121] Subsequently, this application can calculate the spectral signal intensity of each target subsequence.

[0122] For example, this application can obtain the absorption intensity of each wavenumber corresponding to the target subsequence in different infrared spectra. First, the average absorption intensity of each wavenumber in multiple spectra is calculated. Then, the average absorption intensity of all wavenumbers is averaged again to obtain the representative absorption intensity of the target subsequence. Subsequently, the representative absorption intensity of all target subsequences at the detection point is normalized (for example, normalization can be achieved by dividing by the maximum value of the representative absorption intensity of each target subsequence at the detection point) to obtain the spectral signal intensity of the target subsequence.

[0123] The intensity of the spectral signal reflects the strength of the spectral signal within the interference wavenumber range. The smaller the intensity of the spectral signal, the weaker the spectral signal, and the more significant the impact of noise interference on the spectral signal.

[0124] Thus, this application can correct the environmental fluctuation impact coefficient of the target subsequence by using the intensity of the spectral signal, thereby obtaining the actual environmental fluctuation impact coefficient of the target subsequence.

[0125] For example, the coefficient of actual impact of environmental fluctuations satisfies the following formula:

[0126]

[0127] in, For the first The coefficient of the actual impact of environmental fluctuations on each target subsequence For the first The spectral signal intensity of each target subsequence, For the first The environmental fluctuation influence coefficient of each target subsequence. It is a non-negative empirical coefficient used to quantify the correction strength of the influence of spectral signal intensity on environmental fluctuations, for example, 0.3.

[0128] Characterizing signal strength correction coefficients, The smaller the value, the larger the signal strength correction coefficient, thereby amplifying the interference weight in the weak signal region so that the actual impact conforms to the physical law that weak signals are susceptible to noise.

[0129] Ultimately, this application can determine the local dynamic volatility coefficient of the corresponding detection point based on the actual impact coefficient of environmental fluctuations of each target subsequence.

[0130] For example, the local dynamic fluctuation coefficient of the detection point satisfies the following formula:

[0131]

[0132] in, For the first Local dynamic fluctuation coefficient of each detection point The sum of the sequence lengths of all target subsequences. The sequence length of the wave characteristic sequence. For the first The number of target sub-sequences at each detection point For the first The first testing point The coefficient of the actual impact of environmental fluctuations on each target subsequence This is a normalization function (e.g., maximum / minimum normalization) used to map the calculation results to the range of 0 to 1.

[0133] The proportion of the target subsequence in the fluctuation feature sequence is characterized; the larger the proportion, the wider the interference coverage. It represents the sum of the actual impact of all target subsequences, reflecting the overall strength of the interference.

[0134] Step 304: Determine the overall dynamic fluctuation coefficient based on the local dynamic fluctuation coefficient of each detection point.

[0135] For example, this application can use the average value of the local dynamic fluctuation coefficient of each detection point as the overall dynamic fluctuation coefficient to reflect the average interference level of all detection points, so as to avoid the extreme situation of a single detection point from affecting the overall evaluation result. The larger the value of the overall dynamic fluctuation coefficient, the more serious the environmental fluctuation interference in the time dimension of the engineering site.

[0136] Based on the above technical solution, this application can generate a fluctuation characteristic sequence for each detection point based on multiple infrared spectra corresponding to the detection point, so as to evaluate the degree of fluctuation of absorption intensity at each detection point at each wavenumber. Then, based on the fluctuation characteristic sequence, at least one target subsequence is determined, thereby accurately identifying the wavenumber region of strong interference. In this way, the local dynamic fluctuation coefficient of the detection point can be determined based on the absorption intensity at each wavenumber in at least one target subsequence, and the overall dynamic fluctuation coefficient can be determined based on the local dynamic fluctuation coefficient of each detection point. This improves the representativeness of the evaluation results, realizes the accurate quantification of fluctuation analysis, and provides reliable time dimension data support for the overall environmental interference assessment.

[0137] As one possible embodiment of this application, combined with Figure 4 ,like Figure 2 As shown, step 202 above can be achieved through the following steps:

[0138] Step 401: For each detection point, select the target infrared spectrum from the multiple infrared spectrum images corresponding to the detection point.

[0139] Among them, the target infrared spectrum is the spectrum that best reflects the true spectral characteristics of the material at each detection point. This application can screen according to the relative stability of the spectrum. The higher the stability, the less it is affected by instantaneous environmental interference.

[0140] For example, for a given infrared spectrum, this application can calculate the degree of consistency between the infrared spectrum and other infrared spectra, thereby selecting the target infrared spectrum.

[0141] For example, this application can compare the infrared spectrum with each other infrared spectrum wavenumber by wavenumber, calculate the difference in absorption intensity between the two infrared spectra at each wavenumber, and then perform statistical processing on the difference in absorption intensity between the infrared spectrum and each other infrared spectrum at each wavenumber to determine the error value (e.g., expressed as root mean square error (RMSE) or mean absolute error (MAE)). The smaller the error value, the higher the consistency between the infrared spectrum and the overall data, and the more stable the infrared spectrum data.

[0142] Thus, for each detection point, this application can use the above method to select the infrared spectrum with the smallest average value of all corresponding error values ​​from multiple scanning results as the target infrared spectrum for subsequent analysis of spatial heterogeneity between different detection points.

[0143] Step 402: For any two detection points, compare and analyze the target infrared spectra of the two detection points to determine the difference in absorption intensity of the two detection points at each wavenumber.

[0144] The absorption intensity difference value is used to quantify the local difference in spectral signals between two detection points.

[0145] For example, this application can, for any two detection points, traverse all wavenumbers, calculate the absolute value of the difference in absorption intensity of each wavenumber in the target infrared spectrum of the two detection points, and normalize the absolute value of the difference in absorption intensity of each wavenumber (such as maximum and minimum value normalization) to obtain the absorption intensity difference value under each wavenumber.

[0146] Step 403: Determine the spatial heterogeneity coefficient based on the difference in absorption intensity between any two detection points at various wavenumbers.

[0147] In one possible implementation, this application can determine the set of interfering wavenumbers and the interference difference value corresponding to any two detection points based on the difference value of the absorption intensity of the two detection points at each wavenumber. Then, it can determine the spatial heterogeneity coefficient based on the set of interfering wavenumbers and the interference difference value corresponding to any two detection points.

[0148] The interference wavenumber set consists of wavenumbers whose absorption intensity difference is greater than a preset difference threshold. The interference difference value is used to characterize the overall difference in absorption intensity between two detection points. The larger the interference difference value, the more significant the overall spectral difference between the two detection points.

[0149] For example, the preset difference threshold can be determined based on a large amount of experimental data to ensure that normal material differences and environmental interference differences can be effectively distinguished. For example, its value can be 0.4, with the same unit as the absorption intensity unit. The interference difference value can be the average of the absorption intensity difference values ​​of the two detection points at various wavenumbers.

[0150] For example, the spatial heterogeneity coefficient satisfies the following formula:

[0151]

[0152] in, The spatial heterogeneity coefficient is... To determine the number of wavenumbers in the union of the interference wavenumber sets corresponding to all pairs of detection points (i.e., combinations of two detection points), this application can calculate the union of the interference wavenumber sets of all pairs of detection points after obtaining the interference wavenumber sets of any two detection points, thereby determining the number of wavenumbers in the union. , The total wavenumber of the infrared spectrum. For the number of testing points, For the first The first testing point and the first The interference difference value corresponding to each detection point. Normalization functions (such as maximum and minimum value normalization) are used to normalize the calculation results to a range between 0 and 1.

[0153] It represents the proportion of total interfering wavenumbers to total wavenumbers. The larger the proportion, the wider the coverage of space interference wavenumbers. This is the averaging coefficient. This represents the number of combinations of any two detection points, used to balance the impact of the number of detection points on the results. It is the sum of the interference differences between any two detection points, reflecting the overall difference intensity.

[0154] Based on the above technical solution, this application selects the target infrared spectrum from multiple infrared spectra corresponding to each detection point, eliminating the influence of transient interference on the spectrum of a single detection point and ensuring the reliability of the comparison benchmark. Then, for any two detection points, the target infrared spectra of the two detection points are compared and analyzed to determine the difference in absorption intensity of the two detection points at various wavenumbers, which intuitively reflects the degree of spectral deviation between the two detection points. In this way, the spatial heterogeneity coefficient can be determined based on the difference in absorption intensity of any two detection points at various wavenumbers to reflect the overall spatial interference level, providing reliable spatial dimension data support for the overall environmental interference assessment and further improving the accuracy of adaptive filtering parameters.

[0155] As one possible embodiment of this application, combined with Figure 1 ,like Figure 5 As shown, step 103 above can be achieved through the following steps:

[0156] Step 501: Determine the filter window length based on the adaptive filter parameters.

[0157] The choice of filter window length directly affects the denoising effect and detail preservation. This application can use a segmented mapping method to map the adaptive filtering parameters to the filter window length.

[0158] In one possible implementation, when the adaptive filtering parameter is greater than or equal to the first threshold, it indicates that the environmental noise interference is severe. This application can determine the filtering window length to be the first preset length.

[0159] When the adaptive filtering parameter is less than the first threshold and greater than the second threshold, it indicates that the level of environmental noise interference is moderate. This application can determine the filtering window length as the second preset length.

[0160] When the adaptive filtering parameter is less than or equal to the second threshold, it indicates that the environmental noise interference is relatively light. This application can determine the filtering window length as the third preset length.

[0161] The first preset length is greater than the second preset length, and the second preset length is greater than the third preset length.

[0162] For example, the first preset length can be 11, the second preset length can be 9, and the third preset length can be 7, where the window length is always an odd number, conforming to the conventional design of the sliding window filtering algorithm and facilitating the alignment of the window center with the wavenumber point. The first and second thresholds can be determined based on the statistical distribution of a large amount of historical experimental data. For example, the first threshold can be set to 0.6, and the second threshold can be set to 0.3, which can effectively distinguish between high, medium, and low interference scenarios, ensuring the rationality and effectiveness of the window length selection.

[0163] Step 502: Perform sliding window filtering on multiple infrared spectra corresponding to each detection point based on the filtering window length to obtain the filtered infrared spectra.

[0164] In one possible implementation, this application can identify characteristic peak regions and non-characteristic peak regions in each infrared spectrum. Then, based on the filter window length, determine the local adaptive filtering window lengths corresponding to the characteristic peak regions and the non-characteristic peak regions in the infrared spectrum, respectively. This allows for sliding window filtering of the characteristic peak regions based on their respective local adaptive filtering window lengths, and sliding window filtering of the non-characteristic peak regions based on their respective local adaptive filtering window lengths, resulting in a filtered infrared spectrum.

[0165] For example, this application can identify wavenumber intervals that meet the following conditions as characteristic peak regions: the absorption intensity is greater than 1.2 times the absorption intensity of adjacent wavenumber points, and the full width at half maximum (FWHM) of the absorption peak does not exceed 20 cm. -1 Wavenumber intervals that do not meet the above conditions are defined as non-characteristic peak regions.

[0166] For the feature peak region, this application can forcibly reduce the filtering intensity of the feature peak region by introducing a feature peak weight factor, thus preventing the key fingerprint data of the feature peak region from being smoothed out by filtering, thereby affecting the subsequent matching accuracy. For example, the local adaptive filtering window length corresponding to the feature peak region can be the product of the filtering window length and the feature peak weight factor. The value of the feature peak weight factor is between 0 and 1, and can be determined based on historical data statistical analysis, for example, it can be 0.5. In addition, to avoid the problem that the local adaptive filtering window length adjusted based on the feature peak weight factor is still too large, this application can also set a maximum window length for the feature peak region (for example, not exceeding 5). If the local adaptive filtering window length is greater than the maximum window length, then the local adaptive filtering window length is set to the maximum window length.

[0167] For non-characteristic peak regions, this application can directly use the above-mentioned filter window length as the corresponding local adaptive filter window length.

[0168] For example, this application can use the Savitzky-Golay smoothing algorithm for sliding window filtering. This algorithm achieves smoothing by fitting a polynomial using the least squares method within the sliding window and performing convolution operations on the original spectral data within the window. It can effectively suppress random noise while maintaining the width and shape characteristics of the spectral peaks.

[0169] For each original infrared spectrum at each detection point, including both characteristic and non-characteristic peak regions, this application can sequentially slide the window along the wavenumber direction using the determined local adaptive filtering window length, and perform polynomial fitting and smoothing calculations on the absorption intensity sequence within the window to obtain the smoothed absorption intensity value for that wavenumber point. After traversing all wavenumbers, the filtering results for both characteristic and non-characteristic peak regions are merged to obtain the filtered infrared spectrum of that infrared spectrum.

[0170] Repeat the above process to uniformly process all infrared spectra of all detection points, and finally obtain a set of high-quality, low-noise filtered infrared spectra, providing a reliable data foundation for subsequent qualitative analysis of components.

[0171] Based on the above technical solution, this application can dynamically determine the filter window length according to adaptive filter parameters, so that the filter window length can match the current interference intensity. This ensures sufficient denoising effect in strong interference scenarios, suppressing noise introduced by environmental fluctuations and spatial differences. In weak interference scenarios, it prioritizes the preservation of spectral details, avoiding excessive smoothing that could lead to characteristic peak distortion or loss of weak peaks. Compared to traditional filtering schemes, this application can better preserve key information (peak shape, peak position, peak intensity) of spectral characteristic peaks, avoiding the problem of losing effective information while denoising. This ensures that under different interference scenarios, it can effectively filter out environmental noise while completely preserving the characteristic spectral information of the insulation material, providing a crucial guarantee for the accuracy of subsequent detection results.

[0172] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0173] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A novel rapid detection method for thermal insulation materials based on spectral analysis, characterized in that, include: Multiple infrared spectral scans were performed on each testing point of the new thermal insulation material at the engineering site, and multiple infrared spectral images corresponding to each testing point were collected. Infrared spectroscopy is used to characterize the absorption intensity of novel thermal insulation materials at different wavenumbers; Environmental interference analysis was performed based on multiple infrared spectra corresponding to each detection point to determine adaptive filtering parameters, including: The overall dynamic fluctuation coefficient was determined by analyzing the fluctuation of the engineering site based on multiple infrared spectra corresponding to each detection point. Spatial difference analysis of the engineering site was conducted based on multiple infrared spectra corresponding to each detection point to determine the spatial heterogeneity coefficient. The adaptive filtering parameters are determined based on the overall dynamic fluctuation coefficient and the spatial heterogeneity coefficient; the adaptive filtering parameters are used to characterize the severity of environmental noise interference at the engineering site. Among these methods, a volatility analysis of the engineering site is conducted based on multiple infrared spectra corresponding to each detection point to determine the overall dynamic volatility coefficient, including: For each detection point, a fluctuation characteristic sequence for that detection point is generated based on multiple infrared spectra corresponding to that point, including: For each detection point, the absorption intensity of each wavenumber in different infrared spectra is obtained from multiple infrared spectra corresponding to the detection point. For each wavenumber, the absorption intensity fluctuation value corresponding to the wavenumber is determined based on the absorption intensity in different infrared spectra. The fluctuation characteristic sequence of the detection point is determined based on the absorption intensity fluctuation value corresponding to each wavenumber. The fluctuation characteristic sequence includes the absorption intensity fluctuation values ​​corresponding to each wavenumber, sorted in ascending order. The fluctuation characteristic sequence is used to characterize the degree of absorption intensity fluctuation at each wavenumber corresponding to the detection point. Determine at least one target subsequence based on the fluctuation characteristic sequence, including: The screening threshold is determined based on the absorption intensity fluctuation value corresponding to each wavenumber in the wave feature sequence, and target absorption intensity fluctuation values ​​greater than the screening threshold are selected from the wave feature sequence. Adjacent target absorption intensity fluctuation values ​​in the wave feature sequence are merged to obtain at least one target subsequence. The target subsequence is a continuous segment in the wave feature sequence with significantly higher absorption intensity fluctuation values, representing strong environmental fluctuation interference within the wavenumber range. The local dynamic fluctuation coefficient of the detection point is determined based on the absorption intensity at each wavenumber in at least one target subsequence, including: calculating the environmental fluctuation influence coefficient of each target subsequence; the environmental fluctuation influence coefficient satisfies the following formula: in, For the first Environmental fluctuation impact coefficient of each target subsequence For the first The sequence length of a target subsequence, i.e., the number of wavenumbers contained in the target subsequence. The sum of the sequence lengths of all target subsequences. For the first The mean of the target absorption intensity fluctuation values ​​in each target subsequence For the first The maximum value of target absorption intensity fluctuation in each target subsequence; For each target subsequence, the spectral signal intensity of the target subsequence is calculated. The environmental fluctuation impact coefficient is then corrected using the spectral signal intensity to obtain the actual environmental fluctuation impact coefficient of the target subsequence. The actual environmental fluctuation impact coefficient satisfies the following formula: in, For the first The coefficient of the actual impact of environmental fluctuations on each target subsequence For the first The spectral signal intensity of each target subsequence, It is a non-negative empirical coefficient used to quantify the correction strength of the influence of spectral signal intensity on environmental fluctuations; The local dynamic volatility coefficient of the corresponding detection point is determined based on the actual impact coefficient of environmental fluctuations of each target subsequence; the local dynamic volatility coefficient of the detection point satisfies the following formula: in, For the first Local dynamic fluctuation coefficient of each detection point The sum of the sequence lengths of all target subsequences. The sequence length of the wave characteristic sequence. For the first The number of target sub-sequences at each detection point This is a normalization function used to map the calculation result to a range between 0 and 1; The overall dynamic fluctuation coefficient is determined based on the local dynamic fluctuation coefficient of each detection point; the overall dynamic fluctuation coefficient is used to reflect the influence of changes in the engineering site environment over time, namely temperature and humidity fluctuations, airflow disturbances, and changes in gas composition, on the spectral signal; the overall dynamic fluctuation coefficient is used to characterize the degree of interference fluctuation of the spectral signal of the new thermal insulation material over time. Spatial difference analysis was conducted on the engineering site based on multiple infrared spectra corresponding to each detection point to determine the spatial heterogeneity coefficient, including: For each detection point, the target infrared spectrum is selected from multiple infrared spectrum images corresponding to the detection point; For any two detection points, the target infrared spectra of the two detection points are compared and analyzed to determine the difference in absorption intensity of the two detection points at each wavenumber. The spatial heterogeneity coefficient is determined based on the difference in absorption intensity between any two detection points at various wavenumbers, including: For any two detection points, the set of interfering wavenumbers and the interference difference value corresponding to the two detection points are determined based on the difference in absorption intensity at each wavenumber. The set of interfering wavenumbers consists of wavenumbers whose absorption intensity difference value is greater than a preset difference threshold. The interference difference value is used to characterize the overall difference in absorption intensity between the two detection points. The spatial heterogeneity coefficient is determined based on the set of interfering wavenumbers and the interference difference value corresponding to any two detection points. The spatial heterogeneity coefficient is used to reflect the differences in environmental interference at different locations on the engineering site, namely the amount of dust adsorption, surface contamination degree, and local gas concentration at different detection points. The spatial heterogeneity coefficient is used to characterize the degree of interference difference in spectral signals between different detection points on the new thermal insulation material. Based on adaptive filtering parameters, multiple infrared spectra corresponding to each detection point are filtered to obtain the filtered infrared spectra, including: The filter window length is determined based on the adaptive filter parameters; Based on the filtering window length, a sliding window filtering process is performed on multiple infrared spectra corresponding to each detection point to obtain the filtered infrared spectra, including: For each infrared spectrum, the characteristic peak regions and non-characteristic peak regions in the infrared spectrum are identified. Based on the filter window length, the local adaptive filter window lengths corresponding to the characteristic peak regions and non-characteristic peak regions in the infrared spectrum are determined respectively. Sliding window filtering is performed on the characteristic peak regions based on the local adaptive filter window lengths corresponding to the characteristic peak regions in the infrared spectrum, and sliding window filtering is performed on the non-characteristic peak regions based on the local adaptive filter window lengths corresponding to the non-characteristic peak regions, to obtain the filtered infrared spectrum. The test results of the new thermal insulation material are obtained based on the filtered infrared spectrum.

2. The rapid detection method for novel thermal insulation materials based on spectral analysis according to claim 1, characterized in that, Based on the filtered infrared spectrum, the test results of the new thermal insulation material were obtained, including: Extract multiple characteristic peak spectra from the filtered infrared spectrum; The test results of the new thermal insulation material were determined by matching and analyzing the spectra of multiple characteristic peaks with the standard spectra of each material.

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