Regenerant aging failure behavior identification method and system based on differential spectrum extraction

Abstract

The invention relates to the technical field of chemistry and material science, in particular to a regenerant aging failure behavior identification method and system based on differential spectrum extraction. The method comprises the following steps: acquiring infrared spectrum data of a regenerated asphalt sample and an aged asphalt sample at the same stage; carrying out pretreatment on the infrared spectrum data; constructing a characteristic difference spectrum library of the regenerant; extracting a characteristic peak area variation; and obtaining an identification result of the regenerant aging failure behavior by establishing a correlation model of the characteristic peak area variation and the macroscopic performance index. According to the method, high-sensitivity extraction of 3% low-dosage regenerant signals is realized by combining adaptive morphology-quantile regression baseline correction, peak entropy weighted normalization and differential spectrum data enhancement algorithms; a Gaussian-Lorentz mixed peak dividing model and an aging response index model are adopted, microscopic functional group change and macroscopic performance are associated, the aging process of the regenerant is accurately tracked, and a universal technical framework is provided for failure judgment and performance optimization of the regenerant.

Description

Technical Field

[0001] The present invention relates to the technical field of chemistry and materials science, and particularly to a method and system for identifying the aging failure behavior of a regenerant based on differential spectrum extraction. Background Art

[0002] As road infrastructure enters a large-scale maintenance cycle, asphalt regeneration technology has become a key means to extend the pavement life and promote resource recycling. Bio-oil regenerants have shown significant advantages in restoring the properties of aged asphalt due to their green environmental protection and high component activity. However, during long-term service, the regenerant is affected by environmental factors such as heat, oxygen, and ultraviolet rays, and its chemical structure is prone to oxidation and cracking reactions, resulting in a gradual decline in performance, which directly affects the durability of the recycled asphalt. Therefore, accurately identifying the aging failure behavior of the regenerant and revealing the correlation mechanism between its microscopic structure and macroscopic properties are of great significance for improving the design and application level of the regenerant.

[0003] Currently, in the prior art, the aging state of the regenerant is mainly indirectly evaluated by monitoring the macroscopic performance indicators of the recycled asphalt, such as penetration and softening point. However, this method cannot distinguish the respective aging contributions of the asphalt matrix and the regenerant. Especially under the condition of a 3% low dosage, the weak signal of the regenerant is easily interfered and masked by the matrix, resulting in insufficient identification accuracy. In addition, the existing infrared spectroscopy analysis is difficult to separately extract the characteristic functional group information of the regenerant from the composite system, and lacks a standardized processing and dynamic enhancement method for differential spectrum data, making it difficult to extract the characteristic peaks of trace components and unable to establish a quantitative correlation model between the evolution of functional groups and the attenuation of performance. These defects limit the in-depth study and accurate prediction of the aging failure mechanism of the regenerant.

[0004] To address the above problems, the present invention proposes a method for identifying the aging failure behavior of a regenerant based on differential spectrum extraction. By combining an adaptive morphology-quantile regression baseline correction, peak entropy weighted normalization, and differential spectrum data enhancement algorithm, high-sensitivity extraction of the characteristic signals of the low-dosage regenerant is achieved. Furthermore, a Gaussian-Lorentz mixed deconvolution model and an aging response index calculation model are constructed to dynamically correlate the microscopic functional group changes with the macroscopic performance indicators, solving the problem that the existing methods cannot accurately track the aging process of the regenerant. This method not only provides a scientific basis for the failure determination of the regenerant, but also establishes a universal technical framework for the performance optimization and screening of different regenerants. Summary of the Invention

[0005] In view of the defects in the prior art, the present invention provides a method and system for identifying the aging failure behavior of a regenerant based on differential spectrum extraction.

[0006] In a first aspect, a method for identifying the aging failure behavior of a regenerant based on differential spectrum extraction provided by the present invention includes the following steps: obtaining infrared spectral data of a regenerated asphalt sample and an aged asphalt sample at the same stage; preprocessing the infrared spectral data to obtain a preprocessing result, where the preprocessing includes baseline correction and normalization; according to the preprocessing result, using the aged asphalt spectrum as the background spectrum to construct a characteristic differential spectrum library of the regenerant; based on the characteristic differential spectrum library, extracting the change amount of the characteristic peak area of typical functional groups in the regenerant; by establishing a correlation model between the change amount of the characteristic peak area and the macroscopic performance index, obtaining the identification result of the aging failure behavior of the regenerant. By obtaining the infrared spectral data of the regenerated asphalt and the aged asphalt at the same stage and performing baseline correction and normalization, the present invention realizes high-quality standardization processing of the spectral data, providing a reliable basis for subsequent differential spectrum analysis; by using the aged asphalt spectrum as the background spectrum to construct a characteristic differential spectrum library of the regenerant, effectively separating the functional group signal differences between the regenerant and the aged asphalt, significantly improving the accuracy and sensitivity of regenerant characteristic identification; by extracting the change amount of the characteristic peak area of typical functional groups in the regenerant, realizing the dynamic tracking of the chemical structure evolution process of the regenerant at the molecular level and revealing the microscopic mechanism of its aging failure; by establishing a correlation model between the change amount of the characteristic peak area and the macroscopic performance index, quantitatively correlating the microscopic functional group changes with the macroscopic performance attenuation, providing an important method system for the identification and prediction of the aging failure behavior of the regenerant.

[0007] Optionally, the preprocessing of the infrared spectral data to obtain a preprocessing result includes: using a baseline correction method based on adaptive morphology - quantile regression to perform baseline correction processing on the infrared spectral data to obtain infrared spectral data after baseline correction processing; using the peak entropy weighting method to perform normalization processing on the infrared spectral data after baseline correction processing to obtain infrared spectral data after normalization processing, where the normalization processing includes using an improved Burg entropy criterion to locate characteristic peaks; using an improved adaptive iterative reweighted penalty least squares method to perform baseline secondary correction processing on the infrared spectral data after normalization processing to obtain infrared spectral data after baseline secondary correction processing. By adopting a baseline correction method based on adaptive morphology - quantile regression, the present invention effectively suppresses baseline drift and background interference in the infrared spectrum, significantly improving the signal - to - noise ratio and characteristic peak identification accuracy of the spectral data; by introducing the peak entropy weighting method for normalization processing and accurately locating characteristic peaks based on the improved Burg entropy criterion, realizing the adaptive weighted normalization of the spectral data, avoiding the suppression problem of weak peaks by existing normalization methods, and ensuring the complete retention of key functional group information; by using the improved adaptive iterative reweighted penalty least squares method for baseline secondary correction processing, dynamically adjusting the weight function and penalty parameters, solving the problem of complex spectral baseline fluctuations, and further improving the comparability and analysis reliability of the spectral data.

[0008] Optionally, based on the preprocessing results and using the aged asphalt spectrum as the background spectrum, constructing a characteristic differential spectrum library for the rejuvenator includes: establishing a differential spectrum intensity calculation model based on the infrared spectral data after baseline quadratic correction, using the aged asphalt spectrum as the background spectrum; obtaining differential spectrum data according to the normalized differential spectrum intensity calculation model; constructing a differential spectrum data enhancement algorithm model based on the differential spectrum data; obtaining enhanced differential spectrum data through the differential spectrum data enhancement algorithm model; constructing a fingerprint matching degree scoring model using the enhanced differential spectrum data; and constructing a characteristic differential spectrum library for the rejuvenator in combination with the fingerprint matching degree scoring model. By establishing a differential spectrum intensity calculation model with the aged asphalt spectrum as the background spectrum, the present invention effectively separates the superimposed spectral signals of the rejuvenator and the aged asphalt, accurately extracts the characteristic functional group responses of the rejuvenator, and provides high-purity differential spectrum data for subsequent analysis; by constructing a differential spectrum data enhancement algorithm model and using signal enhancement technology to amplify the weak characteristic peaks of the rejuvenator, the detection sensitivity of low-content functional groups is significantly improved; by introducing a fingerprint matching degree scoring model, intelligent screening and classification of differential spectrum data are realized, and in combination with the construction of the characteristic differential spectrum library, a rapid comparison system for the chemical fingerprints of rejuvenators is established, providing a quantifiable discrimination basis for the failure behavior of rejuvenators at different aging stages.

[0009] Optionally, the differential spectrum intensity calculation model satisfies the following expression:

[0010] where is the differential spectrum intensity, is the spectral intensity of the rejuvenator at the wavenumber , is the spectral intensity of the aged asphalt at the wavenumber ; is the dynamic background subtraction factor, is the frequency domain weight function; the differential spectrum data enhancement algorithm model satisfies the following expression:

[0011] where is the enhanced differential spectrum signal, is the differential spectrum intensity, is the convolution operator, is the spectral intensity of the rejuvenator at the wavenumber ; is the spectral intensity of the aged asphalt at the wavenumber ; is the central wavenumber of the target characteristic peak, is the standard deviation of the Gaussian function; the fingerprint matching degree scoring model satisfies the following expression:

[0012] Among them, is the comprehensive score of the regenerant matching degree, is the enhanced differential spectrum signal, is the spectral feature vector of the standard regenerant in the reference spectral library, is the common characteristic peak area between the differential spectrum and the reference spectrum, is the total characteristic peak area of the differential spectrum and the reference spectrum, , is the integration interval. Through constructing a differential spectrum intensity calculation model, the present invention realizes the accurate decoupling of the regenerant and the aged asphalt spectrum, effectively eliminates matrix interference and highlights the regenerant characteristic peaks, significantly improving the accuracy of differential spectrum analysis; by designing a differential spectrum data enhancement algorithm model including derivative operation and Gaussian enhancement, it cleverly combines differential change rate detection and local signal focusing technology, greatly enhancing the recognition ability of weak characteristic peaks and solving the bottleneck problem of insufficient response of existing methods to trace components; by establishing a fingerprint matching degree scoring model integrating spectral graph convolution integral and set similarity metric, it realizes multi-dimensional intelligent comparison between the enhanced differential spectrum and the standard spectral library, providing an important mathematical criterion for the quantitative evaluation of the aging state of the regenerant.

[0013] Optionally, the extraction of the characteristic peak area change amount of typical functional groups in the regenerant based on the characteristic differential spectral library includes: establishing a Gaussian-Lorentz mixed peak separation model based on the characteristic differential spectral library; using the Gaussian-Lorentz mixed peak separation model to fit the characteristic peaks of typical functional groups in the regenerant to obtain a fitting result; establishing a characteristic peak area change amount calculation model according to the fitting result; and extracting the characteristic peak area change amount of typical functional groups in the regenerant through the characteristic peak area change amount calculation model. Through establishing a Gaussian-Lorentz mixed peak separation model to analyze the characteristic differential spectrum, the present invention significantly improves the characteristic peak separation accuracy and quantitative reliability; by using a mixed peak type to fit the characteristic peaks of typical functional groups in the regenerant, it realizes the accurate analysis of complex spectral shapes and provides a high-quality fitting result for subsequent quantitative analysis; by constructing a characteristic peak area change amount calculation model, it converts the peak separation fitting result into a quantifiable functional group evolution index, establishing a bridge from molecular structure change to aging degree evaluation.

[0014] Optionally, the Gaussian-Lorentz mixed peak separation model satisfies the following relational expression:

[0015] Among them, is the mathematical expression of the regenerant characteristic differential spectrum, is the dynamic baseline, is the number of Gaussian peak functions, is the peak height, is the wavenumber at the peak center, is the Gaussian full width at half maximum, is the Lorentzian full width at half maximum, is the mixing coefficient, is the wavenumber; the calculation model of the characteristic peak area change satisfies the following relationship:

[0016] where, is the change amount of the characteristic peak area, , are the fitting areas of the regenerant and the aged asphalt at the th characteristic peak, is the reference value of the corresponding peak area of the standard regenerant, is the weight factor of the wavenumber interval, is the wavenumber interval. By constructing a Gaussian-Lorentzian mixed peak separation model and introducing dynamic baseline correction, the present invention realizes the accurate analysis of overlapping peaks and asymmetric peaks in the characteristic difference spectrum of the regenerant, significantly improves the separation degree and fitting accuracy of complex functional group characteristic peaks; by designing the mixing coefficient to adjust the contribution ratio of the Gaussian and Lorentzian functions, the problem of insufficient adaptability of a single peak type function is solved, providing a flexible mathematical expression for different morphological characteristic peaks; by establishing a calculation model of the characteristic peak area change amount and introducing a weight factor and wavenumber interval correction, the standardized quantitative characterization of the functional group change of the regenerant is realized, effectively eliminating the systematic error caused by instrument fluctuation and test condition difference.

[0017] Optionally, obtaining the recognition result of the aging failure behavior of the regenerant by establishing the correlation model between the characteristic peak area change amount and the macroscopic performance index includes: by the characteristic peak area change amount, combined with the macroscopic performance index, establishing an aging response index calculation model, and the macroscopic performance index includes penetration, softening point, complex shear modulus and creep stiffness; the aging response index calculation model satisfies the following relationship:

[0018] where, is the aging response index, is the change amount of the characteristic peak area, is the initial peak area, , , , respectively represent the initial penetration, softening point, complex shear modulus, creep stiffness, , , , respectively represent the measured performance values of the initial penetration, softening point, complex shear modulus, and creep stiffness after aging, and 、 、 、 are weight coefficients; according to the aging response index calculation model, the aging response index is obtained; based on the aging response index, the identification result of the aging failure behavior of the rejuvenator is obtained. By establishing an aging response index calculation model that integrates the change amount of microscopic characteristic peak area and macroscopic performance indicators, the present invention realizes the cross-scale correlation analysis of the evolution of functional groups at the molecular level and macroscopic pavement performance, and provides a multi-dimensional comprehensive evaluation system for the aging failure behavior of rejuvenators; by designing a composite calculation model including weight coefficient adjustment, the change laws of infrared spectrum characteristic parameters and key performance indicators are effectively integrated; by constructing an aging response index calculation model, the data is converted into a unified evaluation index, realizing the rapid diagnosis of the aging state of rejuvenators.

[0019] Optionally, the obtaining the identification result of the aging failure behavior of the rejuvenator based on the aging response index includes: establishing a coupling correlation coefficient calculation model based on the aging response index; obtaining the coupling correlation coefficient through the coupling correlation coefficient calculation model; evaluating the correlation between the aging of the rejuvenator and the performance attenuation according to the coupling correlation coefficient to obtain the performance change trend; establishing a rejuvenator failure determination model through the aging response index and the performance change trend; obtaining the identification result of the aging failure behavior of the rejuvenator according to the rejuvenator failure determination model. By establishing a coupling correlation coefficient calculation model, the present invention realizes the quantitative correlation analysis between the aging response index and the performance attenuation trend, and reveals the internal connection mechanism between the chemical structure and macroscopic performance of the rejuvenator; by adopting a multi-parameter coupling analysis method, the aging response index and the performance change trend are dynamically correlated, solving the bottleneck problem that it is difficult to accurately evaluate the aging stage of rejuvenators by existing methods; by constructing a rejuvenator failure determination model, the chemical aging index and the engineering performance threshold are integrated, realizing the accurate judgment from molecular evolution to functional failure.

[0020] Optionally, the coupling correlation coefficient calculation model satisfies the following relational expression:

[0021] wherein, is the coupling correlation coefficient, is the aging response index of the th sample, is the sample mean of the aging response index, is the performance attenuation index of the th sample, is the sample mean of the performance attenuation index, is the number of samples; the regenerant failure determination model satisfies the following expression: where is the state of the regenerant, is the critical failure threshold, is the performance retention rate, is the initial performance benchmark, is the material degradation coefficient, is the warning buffer interval, is time. By establishing a coupling correlation coefficient calculation model and adopting the covariance and standard deviation processing method, the present invention realizes the quantification of the dynamic correlation degree between the aging response index and the performance decay index, providing a mathematical tool for revealing the synchronous evolution law of the chemical aging and performance decline of the regenerant; by constructing a three-dimensional failure determination model including the critical failure threshold, the performance retention rate and the degradation coefficient, combining the static threshold criterion with the dynamic change rate analysis, the present invention realizes the multi-level intelligent identification of the regenerant failure state; by setting the warning buffer interval and the grading determination conditions, a progressive warning mechanism from the safe state to the failure state is established, significantly improving the forward-looking and reliability of the regenerant failure state assessment.

[0022] In the second aspect, a regenerant aging failure behavior recognition system based on differential spectrum extraction provided by the present invention includes an input device, a processor, an output device and a memory. The input device, the processor, the output device and the memory are interconnected. Among them, the memory is used to store a computer program, the computer program includes program instructions, the processor is configured to call the program instructions, and the system uses the regenerant aging failure behavior recognition method based on differential spectrum extraction. The system provided by the present invention has a high degree of integration, and the information transmission between components is smooth. By integrating the infrared spectrum differential spectrum extraction and enhancement algorithm, the high-sensitivity detection of the characteristic functional group signals of the regenerant is realized, overcoming the defect of insufficient recognition of trace components in the existing methods, and significantly improving the ability to capture weak changes in the initial stage of aging; by integrating the Gaussian-Lorentz mixed peak fitting model and the dynamic baseline correction technology, the problem of complex spectral peak overlap resolution is solved, providing an accurate quantitative analysis means for the molecular structure evolution of the regenerant; by coupling the change amount of the characteristic peak area with the macroscopic performance index, a cross-scale aging response index model is established, realizing the full-chain correlation analysis from chemical structure changes to engineering performance decline; by developing a failure determination algorithm based on dynamic threshold and change rate, a hierarchical recognition system including safe, warning and failure states is established, greatly improving the accuracy and forward-looking of the regenerant aging state assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1Flow chart of the method for identifying the aging failure behavior of rejuvenator based on differential spectrum extraction according to the embodiment of the present invention; Figure 2 Calibration result diagram of different baseline correction methods according to the embodiment of the present invention; Figure 3 Relationship curve diagram between characteristic peak area and penetration according to the embodiment of the present invention; Figure 4 Structural schematic diagram of the system for identifying the aging failure behavior of rejuvenator based on differential spectrum extraction according to the embodiment of the present invention. Detailed implementation manners

[0024] The specific embodiments of the present invention will be described in detail below. It should be noted that the embodiments described here are only for illustrative purposes and are not used to limit the present invention. In the following description, in order to provide a thorough understanding of the present invention, a large number of specific details are set forth. However, it is obvious to those of ordinary skill in the art that the present invention does not have to employ these specific details. In other instances, well-known circuits, software, or methods have not been specifically described in order to avoid obscuring the present invention.

[0025] Throughout the specification, the reference to "one embodiment", "embodiment", "one example", or "example" means that the specific features, structures, or characteristics described in connection with the embodiment or example are included in at least one embodiment of the present invention. Thus, the phrases "in one embodiment", "in an embodiment", "one example", or "example" appearing throughout the specification do not necessarily all refer to the same embodiment or example. In addition, the specific features, structures, or characteristics may be combined in any suitable combination and / or sub-combination in one or more embodiments or examples. In addition, those of ordinary skill in the art should understand that the drawings provided herein are for illustrative purposes only and are not necessarily drawn to scale.

[0026] Please refer to Figure 1 , the embodiment of the present invention provides a method for identifying the aging failure behavior of rejuvenator based on differential spectrum extraction, and the method includes the following steps: S1. Obtain the infrared spectrum data of the regenerated asphalt sample and the co-stage aged asphalt sample.

[0027] In one embodiment, sample preparation and stage division are first performed to obtain the regenerated asphalt sample and the co-stage aged asphalt sample for infrared spectrum testing.

[0028] Specifically, 70# base asphalt was selected as the raw material, and the Rolling Thin Film Oven Test (RTFOT) and Pressure Aging Vessel Accelerated Aging Test (PAV) were carried out in sequence according to the standard process. RTFOT simulates the performance changes of asphalt during short-term aging, and PAV further simulates the impact of long-term aging on asphalt. Through the treatment of these two steps, the asphalt samples corresponding to the aging stage of recycled asphalt were successfully prepared.

[0029] Furthermore, the prepared aged asphalt samples were taken out, and the rejuvenator was accurately weighed according to a mass fraction of 3%, and it was fully mixed with the aged asphalt. During the mixing process, it is necessary to ensure uniform stirring so that the rejuvenator can be fully dispersed in the aged asphalt, and finally the recycled asphalt samples were prepared.

[0030] Furthermore, the samples and test conditions required for infrared spectroscopy testing were prepared.

[0031] Specifically, appropriate amounts of the prepared aged asphalt samples and recycled asphalt samples were respectively placed on clean sample holders. For viscous samples such as asphalt, a suitable sample preparation method is required, such as heating the sample to a flowing state and then evenly smearing it on the sample holder, ensuring that the thickness of the smeared layer is uniform and the surface is flat, avoiding the appearance of bubbles or uneven thickness, so as to ensure the accuracy of the test results. At the same time, attention should be paid to preventing the sample from being contaminated during the smearing process.

[0032] Furthermore, a Fourier Transform Infrared Spectrometer (FTIR) was used for testing. According to the characteristics of the asphalt sample, appropriate test parameters were set. For example, the scanning range was set to , this range can cover the characteristic absorption peaks of various functional groups in asphalt; the resolution was set to , in order to obtain a clearer spectrogram; the number of scans was set to 32 times. By taking the average value through multiple scans, the signal-to-noise ratio of the spectral signal was improved, and the influence of random errors on the test results was reduced.

[0033] Furthermore, infrared spectroscopy testing was carried out and data was obtained.

[0034] Specifically, before formally testing the samples, a background scan was first performed on the blank sample holder. The sample holder without the sample was placed in the optical path of the FTIR, and the scanning program was started to collect the background spectral data. The purpose of the background scan is to eliminate the interference of the instrument itself, environmental factors, and the sample holder on the test results, and ensure the accuracy and reliability of the sample spectral data obtained in the subsequent tests.

[0035] Further, place the processed aged asphalt sample holder into the optical path of the FTIR, ensuring that the sample completely covers the optical path and the sample surface is perpendicular to the optical path. Start the test program, and the instrument begins to scan the aged asphalt sample to collect its infrared spectral data. During the scanning process, the instrument records the absorption intensity of the sample for infrared light at different wave numbers, and finally generates an infrared spectrogram of the aged asphalt sample, which contains the characteristic absorption information of various chemical bonds and functional groups in the aged asphalt sample.

[0036] Further, after completing the test of the aged asphalt sample, take out the aged asphalt sample holder and replace it with a sample holder containing the recycled asphalt sample, also ensuring that the sample is placed correctly. Start the test program again, scan the recycled asphalt sample, collect its infrared spectral data, and generate an infrared spectrogram of the recycled asphalt sample. Through the above steps, the infrared spectral data of the recycled asphalt sample and the aged asphalt sample at the same stage can be obtained respectively.

[0037] S2. Preprocess the infrared spectral data to obtain a preprocessing result, and the preprocessing includes baseline correction and normalization processing.

[0038] Among them, S2 further includes the following steps: S21. Perform an automatic baseline correction process on the infrared spectral data to obtain the infrared spectral data after baseline correction processing.

[0039] In one embodiment, a baseline correction method based on adaptive morphology - quantile regression is first proposed for performing an automatic baseline correction process on the infrared spectral data.

[0040] Specifically, a top - hat transform is performed using a wavelength - adaptive elliptical structuring element, and the major axis length changes non - linearly with the wavelength. The expression of the major axis length is as follows:

[0041] Among them, is the major axis length, is the maximum window width, is the slope factor, is the wavelength, is the spectral center wavelength.

[0042] Further, for the residual signal after the top - hat transform, calculate the quantile within a sliding window, construct an initial baseline, and optimize it through Tikhonov regularization. The expression of the Tikhonov regularization model is as follows:

[0043] Among them, is the second - order difference operator, is the smoothing factor, is the initial baseline value, is the target baseline value.

[0044] The advantage of this method is that it combines morphological filtering with quantile regression and solves the problem of baseline drift in wide spectral bands through dynamic structural elements.

[0045] S22. Normalize the infrared spectral data after baseline correction to obtain the normalized infrared spectral data.

[0046] In one embodiment, peak entropy weighted normalization is used to process the data after baseline correction.

[0047] Specifically, an improved Burg entropy criterion is used to locate the characteristic peaks. The location formula is as follows:

[0048] where is the intensity value of the spectrum at the th wavelength, is the total energy of the spectrum, is the number of spectral data points, is the improved Burg entropy value, reflecting the degree of dispersion of the spectral energy distribution.

[0049] Furthermore, the entropy weight is calculated for each characteristic peak region, and its calculation expression is as follows:

[0050] where is the th characteristic peak region, is the th entropy weight factor, is the maximum entropy value of the full spectrum, used for weight normalization, is the local entropy value of the region .

[0051] It should be noted that the improved Burg entropy criterion is normalized by introducing the total spectral energy , converting the absolute energy calculation of the traditional Burg entropy into a measure of the relative energy distribution dispersion, enhancing the comparability between spectra of different intensities. At the same time, using to replace the original amplitude highlights the contribution weight of the characteristic peaks, making the entropy value more sensitive to reflect the degree of energy concentration in the peak region. In addition, by calculating the weight as the ratio of the local entropy to the global maximum entropy, adaptive regional normalization is achieved, avoiding the distortion of peak features caused by global uniform scaling.

[0052] Furthermore, the normalized spectral data is obtained, and its normalization expression is as follows:

[0053] Among them, is the normalized spectrum, is the spectrum after baseline correction but before normalization, is the th entropy weight factor, is the peak area.

[0054] The advantage of this method is that it automatically balances the contributions of strong / weak peaks through entropy weights, solving the bias problem of existing normalization methods in heterogeneous samples.

[0055] S23. Using the adaptive iteratively reweighted penalized least squares method, perform a second baseline correction process on the normalized infrared spectrum data to obtain the infrared spectrum data after the second baseline correction process.

[0056] In one embodiment, an improved adaptive iteratively reweighted penalized least squares method (airPLS) is constructed to perform a second baseline correction process, also known as baseline flattening.

[0057] Specifically, the improved airPLS solves for the baseline value by minimizing the following objective function, thereby realizing the second baseline correction process:

[0058] Among them, is the baseline value to be solved, is the weight of the th iteration, is the original spectrum data, is the second-order difference operator, is the L1 penalty coefficient, represents point-by-point multiplication, forcing the baseline to be close to the original spectrum in the non-peak region.

[0059] Furthermore, a dynamic weight update model is established to update the weights. In the th iteration, the weight vector is updated according to the dynamic weight update model. The dynamic weight update model is as follows:

[0060] Among them, is the weight of the th iteration at the th data point, is the second derivative of the spectrum data at the th data point, is the derivative threshold multiplier, represents the median of the absolute value of the second derivative of the full spectrum, represents any data point, represents the point intensity of the original spectrum, represents the baseline estimate of the th iteration, where is the residual scale factor,

[0061] See Figure 2 , Figure 2 which is the calibration result diagram of different baseline correction methods of the present invention. Figure 2 It shows the relationship between absorbance and wavenumber after being untreated, processed by the OMNIC program, and processed by the improved airPLS method. The absorbance is a dimensionless parameter, and the OMNIC is software for infrared spectrum data acquisition and analysis.

[0062] From Figure 2 it can be seen that compared with being untreated and processed by conventional software programs, the improved airPLS method can correct baseline drift more thoroughly. For example, in the wavenumber region, it can dynamically identify background noise and retain real signals. In particular, it optimizes weak peak detection. For example, in the wavenumber region, it improves data reliability.

[0063] It should be noted that the improvement of the airPLS of the present invention lies in: changing the existing L2 penalty term to the L1 norm, enhancing the sparse constraint on the sudden change of baseline curvature, and improving the fitting ability for complex baselines; introducing a dynamic derivative threshold mechanism, adaptively identifying peak regions and non-peak regions through the median of the second derivative of the spectrum, and avoiding manually setting a fixed threshold.

[0064] Furthermore, through the objective function and the dynamic weight update model, infrared spectrum data after baseline secondary correction processing is obtained.

[0065] The advantage of this method is that it can effectively retain important real signals by using the derivative constraint mechanism. Compared with the existing airPLS method, it achieves a significant reduction in error and has better performance.

[0066] S3. According to the preprocessing result, using the aged asphalt spectrum as the background spectrum, construct a characteristic difference spectrum library of the rejuvenator.

[0067] In one embodiment, according to the preprocessing result, using the aged asphalt spectrum as the background spectrum, establish a difference spectrum intensity calculation model, and the difference spectrum intensity calculation model satisfies the following expression:

[0068] where is the difference spectrum intensity, is the rejuvenator at wavenumber The spectral intensity at is the spectral intensity of the aged asphalt at the wavenumber ; is the dynamic background subtraction factor, an adjustment coefficient that varies with the wavenumber, dynamically calculated through the intensity gradient of the aging characteristic peak, and is used to accurately subtract the background interference of the aged asphalt. is the frequency-domain weight function, which assigns higher weights in the characteristic peak region of the rejuvenator to enhance the contribution of the target functional groups.

[0069] Furthermore, a differential spectrum data enhancement algorithm model is constructed. The original differential spectrum is subjected to feature extraction through the convolution kernel . The differential spectrum data enhancement algorithm model satisfies the following expression:

[0070] where is the enhanced differential spectrum signal, and features are extracted through convolution operations. is the convolution operator, representing the integral overlap calculation between functions. is a composite convolution kernel, composed of a derivative term and a Gaussian term, and satisfies the following expression:

[0071] where is the spectral intensity of the rejuvenator at the wavenumber ; is the spectral intensity of the aged asphalt at the wavenumber ; is the central wavenumber of the target characteristic peak. is the standard deviation of the Gaussian function, which determines the width of the wavenumber range for feature peak extraction.

[0072] The advantage of this method is that it uses the derivative term to capture the spectral line slope difference between the rejuvenator and the aged asphalt, and the Gaussian term focuses on the characteristic peak region. Compared with conventional wavelet denoising, it can more effectively retain the functional group fingerprint information and achieve the directional enhancement of weak characteristic signals.

[0073] Furthermore, a fingerprint matching degree scoring model is constructed to establish a fingerprint differential spectrum library of rejuvenators. The fingerprint matching degree scoring model realizes the identification of rejuvenators through the matching degree scoring of typical functional group feature vectors. The fingerprint matching degree scoring model satisfies the following expression:

[0074] where is the comprehensive scoring of the rejuvenator matching degree. is the enhanced differential spectrum signal. is the spectral feature vector of the standard rejuvenator in the reference spectrum library. is the common characteristic peak area between the differential spectrum and the reference spectrum. is the total characteristic peak area of the differential spectrum and the reference spectrum, 、 is the integration interval, covering the characteristic wavenumber range of the target functional group.

[0075] The advantage of this method is that it uses a fingerprint matching degree scoring model to integrate two parameters, namely spectral intensity integration and spectral shape topological similarity. Compared with the existing single peak height ratio method, it significantly improves the specificity of regenerant identification, especially suitable for distinguishing regenerants with similar chemical compositions.

[0076] S4. Based on the characteristic differential spectrum library, extract the change amount of the characteristic peak area of the typical functional groups in the regenerant.

[0077] In one embodiment, first construct an adaptive Gaussian-Lorentz mixed peak splitting model, and the expression of this model is as follows:

[0078] Where, is the mathematical expression of the characteristic differential spectrum of the regenerant, used to fit the preprocessed infrared spectral data, is the dynamic baseline, using piecewise polynomial fitting, is the number of Gaussian peak functions, is the th Gaussian peak function, satisfying the following expression:

[0079] Where, is the peak height, is the peak center wavenumber, is the Gaussian full width at half maximum, is the th Lorentz peak function, satisfying the following expression:

[0080] Where, is the Lorentz full width at half maximum, is the mixing coefficient, dynamically adjusting the ratio of the Gaussian and Lorentz components, calculated through the peak shape asymmetry degree, and the relevant expressions are as follows:

[0081]

[0082] Where, 、 respectively represent the absolute values of the slopes on the left and right sides of the peak center.

[0083] The advantages of this method are that it adaptively adjusts the Gaussian / Lorentz ratio according to the peak shape asymmetry, overcoming the defect of the existing method using fixed empirical values; for the complex system of regenerant-aged asphalt, the baseline fitting interval is automatically divided according to the distribution of characteristic peaks, overcoming the defect of the existing method using a global baseline that leads to fitting deviation.

[0084] Furthermore, a calculation model for the change amount of characteristic peak area is established, and the calculation model for the change amount of characteristic peak area satisfies the following expression:

[0085] Wherein, is the change amount of characteristic peak area, 、 are the fitting areas of the regenerant and the aged asphalt at the th characteristic peak, is the reference value of the corresponding peak area of the standard regenerant (unaged), Wavenumber interval The weight factor of is determined by the signal-to-noise ratio of the characteristic peak.

[0086] The advantages of this method are that it dynamically weights using the signal-to-noise ratio of the characteristic peak to improve the reliability of weak peaks; by introducing the reference value of the corresponding peak area of the standard regenerant, the batch difference of the regenerant is eliminated.

[0087] S5. By establishing the correlation model between the change amount of the characteristic peak area and the macroscopic performance index, the identification result of the aging failure behavior of the regenerant is obtained.

[0088] In one embodiment, first, an aging response index calculation model based on the change amount of the characteristic peak area and the macroscopic performance index is established. The macroscopic performance indexes include penetration, softening point, complex shear modulus, and creep stiffness; the aging response index calculation model satisfies the following expression:

[0089] Wherein, is the aging response index, is the change amount of the characteristic peak area, is the initial peak area, 、 、 、 respectively represent the initial penetration, softening point, complex shear modulus, and creep stiffness, 、 、 、 respectively represent the measured performance values of the initial penetration, softening point, complex shear modulus, and creep stiffness after aging, 、 、 , , is the weight coefficient. The penetration is the depth that a standard needle vertically penetrates an asphalt specimen within 5 seconds under specified temperature and load, expressed in 0.1 as the unit; the softening point is the temperature at which the asphalt material begins to soften and reaches a certain sag amount under specific conditions; the complex shear modulus is the complex modulus that describes the stress-strain relationship of the asphalt material under dynamic shear action, including a real part and an imaginary part; the creep stiffness is the parameter that describes the change of strain with time when the asphalt material is under a constant stress at low temperature, reflecting the anti-deformation ability and anti-cracking performance of the asphalt material at low temperature.

[0090] It should be noted that if the characteristic peak area changes significantly and has a good linear relationship with all macroscopic performance indicators, it can be used to judge the oxidative failure of the regenerant. That is to say, if there is no good linear relationship with one of the macroscopic performance indicators, the regenerant has undergone oxidative failure.

[0091] Please refer to Figure 3 , in a specific embodiment, the relationship curve between the characteristic peak area and the penetration is given. From Figure 3 it can be seen that the linear expression is as follows:

[0092] wherein, represents the penetration, represents the characteristic peak area, and the correlation coefficient between the penetration and the characteristic peak area means that there is a good linear relationship in this relationship curve.

[0093] It should be noted that when the actually measured penetration significantly deviates from the predicted value of the regression equation, it indicates that the regenerant has been oxidized and failed.

[0094] The advantage of this method is that it combines chemical characteristic peaks with multiple macroscopic performance indicators, overcoming the limitations of existing indicators.

[0095] Furthermore, based on the aging response index calculation model, a coupling correlation coefficient calculation model is established to evaluate the non-linear correlation between regenerant aging and performance degradation. The coupling correlation coefficient calculation model satisfies the following expression:

[0096] wherein, is the coupling correlation coefficient, measuring the statistical correlation between and performance degradation, and its value range is , the closer it is to indicating a stronger correlation, is the aging response index of the th sample, is the sample mean of the aging response index, is the th sample's performance degradation index, which can be the penetration loss rate, softening point growth rate, or other macroscopic performance degradation amounts, is the sample mean of the performance degradation index, representing the average level of performance degradation, is the number of samples, used to calculate the statistical correlation.

[0097] The advantage of this method is that it quantifies the coupling degree between aging and performance by coupling the correlation coefficient, which is better than the existing Pearson correlation coefficient and can capture non - linear relationships.

[0098] Furthermore, through the joint criterion of the threshold and the performance change trend, a regenerant failure determination model is established. The regenerant failure determination model satisfies the following expression: where, is the state of the regenerant, is the critical failure threshold, is the performance retention rate, is the initial performance benchmark, is the material degradation coefficient, is the warning buffer interval, is the time.

[0099] Furthermore, using the regenerant failure determination model, identify the aging failure behavior of the regenerant, that is, when is in the failure state, the regenerant ages and fails.

[0100] The advantage of this method is that it combines the threshold and the performance change rate to construct a multi - level failure warning mechanism, realizing the accurate quantitative evaluation of the material degradation process, significantly improving the sensitivity and reliability of the regenerant state identification, and providing a theoretical basis for predictive maintenance.

[0101] Please refer to Figure 4 , Figure 4 which is the structural schematic diagram of a regenerant aging failure behavior identification system based on differential spectrum extraction in an embodiment of the present invention. The system includes an input device, a processor, an output device, and a memory. The input device, the processor, the output device, and the memory are interconnected. Among them, the memory is used to store computer programs, the computer programs include program instructions, the processor is configured to call the program instructions, and the system uses the above - mentioned regenerant aging failure behavior identification method based on differential spectrum extraction.

[0102] In this embodiment, the input device includes an infrared spectrometer, a mechanical property tester, and a data interface module; the infrared spectrometer is used to collect infrared spectral data of the regenerant and the aged asphalt; the mechanical property tester is used to obtain macroscopic property test data, and use the data interface module to receive and store these data to provide input for subsequent correlation analysis; the input device supports data format standardization to ensure that the collected and received data is compatible with the processor.

[0103] The processor includes a preprocessing module, a differential spectrum analysis module, a peak fitting module, a correlation modeling module, and a failure determination module; the preprocessing module is used to denoise, correct, and enhance the original spectral data to improve the signal-to-noise ratio; the differential spectrum analysis module is used to extract the differential spectrum signals of the characteristic functional groups of the regenerant and construct a differential spectrum database; the peak fitting module is used to quantify the correlation between the change in peak area and the macroscopic properties; the correlation modeling module is used to calculate the aging response index in real time; the failure determination module is used to perform failure warning in combination with the performance decay trend.

[0104] The output device includes a display terminal, a report generation module, and a warning prompt module; the display terminal is used to intuitively display the aging degree of the regenerant, such as the safe / warning / failure state; the report generation module is used to generate a complete report containing differential spectrum analysis, peak area change, and aging response index data; the warning prompt module is used to give a warning prompt when necessary.

[0105] The memory uses a high-speed solid-state drive, which has the characteristics of fast read and write speed, large capacity, and high reliability. It is mainly used to store the data input by the input device and the result data processed by the processor, and can meet the needs of large data volume storage.

[0106] In summary, the present invention realizes the high-sensitivity extraction of the characteristic signals of the low-dosage regenerant by combining the adaptive morphology-quantile regression baseline correction, peak entropy weighted normalization, and differential spectrum data enhancement algorithms; further constructs a Gaussian-Lorentz mixed peak fitting model and an aging response index calculation model to dynamically correlate the microscopic functional group changes with the macroscopic performance indicators, and solves the problem that the existing methods cannot accurately track the aging process of the regenerant. This method not only provides a scientific basis for the failure determination of the regenerant, but also establishes a universal technical framework for the performance optimization and screening of different regenerants.

[0107] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the various embodiments of the present invention, and they should all be covered within the scope of the claims and the description of the present invention.

Claims

1. A method for identifying aging failure behavior of regeneration agent based on difference spectrum extraction, characterized in that: The method comprises the following steps: Obtain infrared spectral data of recycled asphalt samples and aged asphalt samples at the same stage; Preprocessing the infrared spectrum data to obtain a preprocessing result, wherein the preprocessing includes baseline correction and normalization processing; According to the pretreatment results, the characteristic difference spectrum library of the regeneration agent is constructed with the spectrum of the aged asphalt as the background spectrum; Based on the characteristic difference spectrum library, extracting the characteristic peak area change of typical functional groups in the regeneration agent; By establishing a correlation model between the characteristic peak area variation and the macro performance index, the identification result of the aging failure behavior of the regeneration agent is obtained.

2. The method for identifying aging failure behavior of regeneration agent based on difference spectrum extraction according to claim 1 is characterized in that: The preprocessing of the infrared spectrum data to obtain the preprocessing result comprises: Performing baseline correction processing on the infrared spectrum data using a baseline correction method based on adaptive morphology-quantile regression to obtain infrared spectrum data after baseline correction processing; Using a peak entropy weighting method, normalizing the infrared spectrum data after the baseline correction process to obtain normalized infrared spectrum data, wherein the normalization process includes locating characteristic peaks using an improved Burg entropy criterion; The improved adaptive iterative reweighted penalty least square method is used to perform baseline correction processing on the normalized infrared spectrum data to obtain infrared spectrum data after baseline secondary correction processing.

3. The method for identifying aging failure behavior of regeneration agent based on difference spectrum extraction according to claim 1 is characterized in that: According to the pretreatment result, taking the aged asphalt spectrum as the background spectrum, constructing the characteristic difference spectrum library of the regeneration agent comprises: According to the infrared spectrum data after the baseline secondary correction, the spectrum of aged asphalt is used as the background spectrum to establish the difference spectrum intensity calculation model; Obtaining difference spectrum data according to the difference spectrum intensity calculation model; Based on the difference spectrum data, construct a difference spectrum data enhancement algorithm model; Obtaining signal-enhanced difference spectrum data through the difference spectrum data enhancement algorithm model; Using the signal-enhanced difference spectrum data, a fingerprint matching scoring model is constructed; Combined with the fingerprint matching degree scoring model, a characteristic difference spectrum library of the regeneration agent is constructed.

4. The method for identifying aging failure behavior of regeneration agent based on difference spectrum extraction according to claim 3 is characterized in that: The difference spectrum intensity calculation model satisfies the following expression: ， in, is the difference spectrum intensity, is the regeneration agent at wave number The spectral intensity at For aged asphalt in wave number The spectral intensity at is the dynamic background subtraction factor, is a frequency domain weight function; the difference spectrum data enhancement algorithm model satisfies the following expression: ， in, is the enhanced difference spectrum signal, is the difference spectrum intensity, is the convolution operator, is the regeneration agent at wave number The spectral intensity at For aged asphalt in wave number The spectral intensity at is the central wave number of the target characteristic peak, is the standard deviation of the Gaussian function; the fingerprint matching score model satisfies the following expression: ， in, is the comprehensive score of regeneration agent matching, is the enhanced difference spectrum signal, is the spectral feature vector of the standard regenerant in the reference library, is the common characteristic peak area between the difference spectrum and the reference spectrum, is the overall characteristic peak area of ​​the difference spectrum and the reference spectrum, , is the integration interval.

5. The method for identifying aging failure behavior of regeneration agent based on difference spectrum extraction according to claim 1 is characterized in that: The characteristic peak area variation of the typical functional groups in the extraction regeneration agent based on the characteristic difference spectrum library includes: Based on the characteristic difference spectrum library, a Gauss-Lorentz mixed peak separation model is established; Using the Gauss-Lorentz mixed peak splitting model, the characteristic peaks of typical functional groups in the regeneration agent are fitted to obtain fitting results; According to the fitting results, a characteristic peak area change calculation model is established; The characteristic peak area variation calculation model is used to extract the characteristic peak area variation of typical functional groups in the regeneration agent.

6. The method for identifying aging failure behavior of regeneration agent based on difference spectrum extraction according to claim 5 is characterized in that: The Gauss-Lorentz mixed peak splitting model satisfies the following relationship: ， in, is the mathematical expression of the regeneration agent characteristic difference spectrum, is a dynamic baseline, is the number of Gaussian peak functions, For peak height, is the peak center wave number, is the Gaussian half-peak width, is the Lorentz half-peak width, is the mixing coefficient, is the wave number; the characteristic peak area change calculation model satisfies the following relationship: ， in, is the characteristic peak area change, , As the rejuvenating agent and aged asphalt The fitted area of ​​the characteristic peaks, is the corresponding peak area reference value of the standard regenerant, is the weight factor of the wave number interval, is the wave number interval.

7. The method for identifying aging failure behavior of regeneration agent based on difference spectrum extraction according to claim 1 is characterized in that: The identification result of the aging failure behavior of the regeneration agent obtained by establishing the correlation model between the characteristic peak area change and the macro performance index includes: The aging response index calculation model is established by combining the characteristic peak area change with the macro performance index, wherein the macro performance index includes needle penetration, softening point, complex shear modulus and creep stiffness; the aging response index calculation model satisfies the following relationship: ， in, is the aging response index, is the characteristic peak area change, is the initial peak area, , , , They represent initial penetration, softening point, complex shear modulus, and creep stiffness, respectively. , , , Respectively represent the measured performance values ​​of initial penetration, softening point, complex shear modulus, and creep stiffness after aging. , , , , is the weight coefficient; Obtaining an aging response index according to the aging response index calculation model; Based on the aging response index, an identification result of the aging failure behavior of the regeneration agent is obtained.

8. The method for identifying aging failure behavior of regeneration agent based on difference spectrum extraction according to claim 7 is characterized in that: The obtaining of the identification result of the aging failure behavior of the regeneration agent based on the aging response index includes: Based on the aging response index, a coupling correlation coefficient calculation model is established; Obtaining a coupling correlation coefficient through the coupling correlation coefficient calculation model; According to the coupling correlation coefficient, the correlation between the aging of the regenerant and the performance attenuation is evaluated to obtain the performance change trend; Establishing a regeneration agent failure determination model based on the aging response index and the performance change trend; According to the regeneration agent failure determination model, the identification result of the regeneration agent aging failure behavior is obtained.

9. The method for identifying aging failure behavior of regeneration agent based on difference spectrum extraction according to claim 8 is characterized in that: The coupling correlation coefficient calculation model satisfies the following relationship: ， in, is the coupling correlation coefficient, For the The aging response index of the samples, is the sample mean of the aging response index, For the The performance degradation index of samples is is the sample mean of the performance decay index, is the number of samples; the regeneration agent failure determination model satisfies the following expression: ， in, is the state of the regeneration agent, is the critical failure threshold, is the performance retention rate, For the initial performance benchmark, is the material degradation coefficient, For the warning buffer zone, For time.

10. A regeneration agent aging failure behavior identification system based on differential spectrum extraction, the system using a regeneration agent aging failure behavior identification method based on differential spectrum extraction according to any one of claims 1 to 9, characterized in that: The system includes an input device, a processor, an output device and a memory, wherein the input device, the processor, the output device and the memory are connected to each other, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions.

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