Asphalt quality rapid detection method and system based on infrared spectroscopy

Abstract

The present invention relates to the field of quality testing technology, specifically to a method and system for rapid detection of asphalt quality based on infrared spectroscopy, comprising the following steps: based on an asphalt sample, setting the wavelength range and sensitivity of the infrared spectrum, identifying the emission spectrum and absorption spectrum of the asphalt sample, and generating initialization spectral data by processing signals and noise. In the present invention, through refined adjustment, the noise of the spectral data is effectively processed, the reliability of the test data is increased, key spectral peaks are marked and compared with historical standard spectra, and an intuitive comparison benchmark is provided for asphalt quality, which not only speeds up the detection process, but also improves the accuracy of the detection results. Rapid and accurate asphalt quality detection greatly improves the compliance of road construction materials and the safety of roads, ensures that the materials used strictly meet the engineering requirements, and has a significant impact on accelerating project progress and reducing maintenance costs.

Description

Technical Field

[0001] The present invention relates to the technical field of quality testing, and in particular to a method and system for rapid detection of asphalt quality based on infrared spectroscopy. Background Art

[0002] The field of quality testing technology is used to evaluate the performance and safety of various materials and products. It includes not only the analysis of chemical composition, but also the determination of physical properties, such as hardness, viscosity and melting point. The purpose of quality testing is to ensure that the product meets the predetermined quality standards during design, manufacturing and use. In chemical engineering and materials science, quality testing is particularly important because it helps manufacturers comply with strict industry regulations and standards while ensuring consumer safety and product reliability.

[0003] Among them, the rapid detection method of asphalt quality based on infrared spectroscopy is a technology that uses infrared spectroscopy to analyze the composition and quality of asphalt samples. Infrared spectroscopy can identify and measure specific absorption peaks of organic compounds and chemicals in asphalt, providing detailed information about chemical properties. The main purpose is to quickly evaluate the quality of asphalt in road construction and maintenance, ensure that the materials used meet engineering requirements, and improve the durability and safety of roads. Through infrared spectroscopy technology, accurate test results can be obtained in a short time, accelerating the decision-making process and construction progress.

[0004] Existing quality testing technologies have obvious deficiencies in the rapid and accurate testing of asphalt. They focus on basic chemical composition analysis and physical property testing, such as hardness and viscosity determination, which is time-consuming and cannot provide comprehensive information about material performance in real time. Especially in the field of road construction, where construction decisions need to be made quickly, delays lead to project extensions or the use of substandard materials, affecting the quality and durability of roads. Traditional quality testing technologies have difficulty in quickly identifying small but critical changes in composition when dealing with asphalt with complex chemical composition, resulting in unforeseen differences in material performance in actual applications, such as early damage or performance degradation. The limitation is that it requires a long preparation and processing time, and lacks the ability to conduct rapid and comprehensive analysis, which is particularly disadvantageous in urgent or time-sensitive projects, posing a potential risk to ensuring the quality and safety of road construction. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a method and system for rapid detection of asphalt quality based on infrared spectroscopy.

[0006] In order to achieve the above object, the present invention adopts the following technical scheme, a rapid detection method of asphalt quality based on infrared spectroscopy, comprising the following steps:

[0007] S1: Based on the asphalt sample, set the wavelength range and sensitivity of the infrared spectrum, identify the emission spectrum and absorption spectrum of the asphalt sample, and generate initialization spectrum data by processing the signal and noise;

[0008] S2: According to the initialized spectral data, quickly detect the asphalt sample, identify the preset infrared spectral features, mark the key infrared spectral peaks, and compare with the historical standard spectrum to obtain a characteristic spectrum diagram;

[0009] S3: performing normalization on the characteristic spectrum graph, proportionally adjusting and normalizing the intensity of the spectrum peak, checking the consistency of the data, and calculating the normalized index after processing to obtain normalized spectrum data;

[0010] S4: using the normalized spectral data, quickly testing the quality of asphalt, analyzing the fitting spectral fingerprint of the differentiated asphalt, comparing the asphalt type with the known standard, verifying the asphalt type, and outputting an asphalt type classification table;

[0011] S5: using the asphalt type classification table, by analyzing the chemical composition of asphalt, quickly testing the quality of asphalt, evaluating the performance indicators of asphalt, and obtaining the quality evaluation results by referring to the stability of the components and the consistency of the performance;

[0012] S6: Based on the quality assessment results and in combination with the real-time application scenarios of the asphalt, the expected service life, maintenance cost, and replacement frequency of the differentiated asphalt are analyzed to generate an asphalt adjustment plan.

[0013] As a further scheme of the present invention, the initialized spectral data includes the emission peak position, absorption band width and absorption peak intensity, the characteristic spectrum graph includes the peak wavelength, peak height, peak width and matching score, the normalized spectral data includes the normalized peak intensity, standardization index and consistency index, the asphalt type classification table includes the type name, spectral characteristics and fit rating, the quality assessment results include the component content ratio, performance consistency rating and stability score, and the asphalt adjustment plan includes service life prediction, maintenance cycle and cost-benefit analysis.

[0014] As a further solution of the present invention, based on the asphalt sample, the wavelength range and sensitivity of the infrared spectrum are set, the emission spectrum and absorption spectrum of the asphalt sample are identified, and the steps of generating the initialization spectrum data by processing the signal and the noise are specifically as follows:

[0015] S101: Based on the asphalt sample, according to the control panel of the infrared spectrometer, the wavelength range is set, the sensitivity is adjusted to match the absorption band and emission peak of the asphalt, and the parameters of the infrared spectrum are configured to generate a set parameter spectrum;

[0016] S102: using the set parameter spectrum and the automatic signal optimization function of the infrared spectrometer, adjusting the spectrum signal, reducing background noise, optimizing the quality of the spectrum data, and obtaining an optimized original spectrum;

[0017] S103: According to the optimized original spectrum, the spectral data is imported, and the format and resolution of the data are adjusted to match the technical requirements of the rapid detection of asphalt quality, so as to generate initialization spectral data.

[0018] As a further solution of the present invention, according to the initialized spectral data, the asphalt sample is quickly detected, the preset infrared spectral features are identified, the key infrared spectral peaks are marked, and the historical standard spectra are compared to obtain the characteristic spectrum diagram. Specifically, the steps are as follows:

[0019] S201: using the initialized spectral data to perform an initialization scan of asphalt quality, automatically detecting and marking preset infrared spectral features, identifying target absorption peaks and emission peaks of the infrared spectrum, identifying characteristic peaks, and generating a marked characteristic spectrum;

[0020] S202: iteratively analyzing the intensity and wavelength of each marked peak through the marked characteristic spectrum, performing quantitative analysis on the data of the marked peak, calculating the peak parameters, and obtaining a quantitative characteristic spectrum;

[0021] S203: Based on the quantitative characteristic spectrum, compare with the historical standard spectrum, analyze the fit with the known samples, sort the fit scores, identify the type and quality of asphalt, and generate a characteristic spectrum graph.

[0022] As a further solution of the present invention, the characteristic spectrum graph is normalized, the intensity of the spectrum peak is proportionally adjusted and standardized, the consistency of the data is checked, and the normalized index after processing is calculated. The steps of obtaining normalized spectrum data are specifically as follows:

[0023] S301: using the characteristic spectrum, adjusting the ratio of the infrared spectrum peaks, proportionalizing the intensity of each peak with the intensity of the standard spectrum, checking that the infrared spectrum data is scaled at the same ratio, and generating a proportionally adjusted spectrum;

[0024] S302: adjusting the spectrum based on the ratio, performing standardization processing, converting the adjusted spectrum data into a standardized score, and adjusting the scale of the data by calculating the standard deviation and the mean to obtain standardized spectrum data;

[0025] S303: Perform consistency check and normalization index calculation on the standardized spectral data to verify that the standardized spectral data matches the asphalt quality standard, calculate the normalization index to evaluate the statistical consistency of the data, and generate normalized spectral data.

[0026] As a further solution of the present invention, the steps of using the normalized spectral data to quickly detect the quality of asphalt, analyze the fit spectral fingerprint of differentiated asphalt, compare the asphalt type with the known standard, verify the asphalt type, and output the asphalt type classification table are as follows:

[0027] S401: using the normalized spectral data and a spectral matching algorithm to perform a rapid test of asphalt quality, and generating a fit analysis result by comparing the spectral data with a fingerprint in a preset spectrum;

[0028] S402: According to the fit analysis result, compare the asphalt type, match the sample spectrum with the known standard spectrum, analyze the best matching type of the asphalt sample, identify the spectral characteristics of the differentiated asphalt type, and obtain the asphalt type verification result;

[0029] S403: Based on the asphalt type verification result, identify the type, matching degree and associated spectral characteristics of the detected asphalt sample, and output an asphalt type classification table by integrating and formatting asphalt quality related data.

[0030] As a further solution of the present invention, the formula of the spectrum matching algorithm is as follows:

[0031]

[0032] Among them, S is the weighted average fitness score, R i is the spectral value in the normalized spectral data, T i is the target spectrum value in the preset spectrum, n is the total number of data points, w i is the weight coefficient for each data point.

[0033] As a further solution of the present invention, the asphalt type classification table is used to analyze the chemical composition of asphalt, quickly detect the quality of asphalt, evaluate the performance indicators of asphalt, and refer to the stability of the components and the consistency of the performance to obtain the quality evaluation results. Specifically, the steps are as follows:

[0034] S501: According to the asphalt type classification table, quickly identify and record the key chemical components of each asphalt type, analyze the deviation of asphalt quality and the cause of the deviation by comparing with the historical chemical composition standards, and generate chemical composition analysis results;

[0035] S502: Based on the chemical composition analysis results, evaluate the performance indicators of each asphalt, calculate the component stability and performance consistency, identify the quality fluctuation of the differentiated components and their impact on the asphalt performance, and obtain the performance stability evaluation results;

[0036] S503: Using the performance stability evaluation results, evaluate the quality of asphalt, combine multiple test data, analyze the overall quality status and recommended use range of asphalt, and output the quality evaluation results.

[0037] As a further solution of the present invention, according to the quality assessment results, combined with the real-time application scenarios of asphalt, the expected service life, maintenance cost and replacement frequency of differentiated asphalt are analyzed, and the steps of generating an asphalt adjustment plan are specifically as follows:

[0038] S601: Based on the quality assessment result, by analyzing the trend of historical data and performance indicators, simulating the service life of differentiated types of asphalt samples, calculating the expected service life, and generating an expected service life analysis result;

[0039] S602: Calculate the asphalt maintenance cost and replacement frequency using the expected service life analysis result, and obtain the maintenance and replacement cost analysis result by referring to the material cost, labor and time cost, and the frequency of asphalt maintenance;

[0040] S603: Based on the maintenance and replacement cost analysis results, and referring to the application scenarios and environment of asphalt, a targeted asphalt adjustment plan is formulated, including asphalt usage, maintenance plan and preventive measures, and the asphalt adjustment plan is output.

[0041] The asphalt quality rapid detection system based on infrared spectroscopy is used to execute the asphalt quality rapid detection method based on infrared spectroscopy. The system comprises:

[0042] The spectrum analysis module adjusts the wavelength range and sensitivity of the infrared spectrum based on the asphalt sample, captures the emission and absorption spectra of the asphalt, processes the original signal to avoid noise interference, and generates original spectrum data;

[0043] The feature recognition module uses the original spectral data to analyze and mark key infrared spectral features, identify absorption and emission peaks, and compare them with historical spectral features to obtain a feature spectrum graph;

[0044] The normalization processing module uses the characteristic spectrum graph to perform intensity ratio adjustment and normalization processing of the spectrum peak, check data consistency, and calculate the normalization index to obtain normalized spectrum data;

[0045] The quality analysis module performs rapid detection of asphalt quality based on the normalized spectral data, analyzes the fit spectral fingerprint of the differentiated asphalt, compares and verifies the asphalt type, and outputs an asphalt type classification table;

[0046] The strategy adjustment module is based on the asphalt type classification table and combines the real-time application scenarios of asphalt to analyze the expected service life, maintenance cost and replacement frequency, evaluate the performance indicators and component stability of asphalt, and generate an asphalt adjustment plan.

[0047] Compared with the prior art, the advantages and positive effects of the present invention are:

[0048] In the present invention, by carefully setting the wavelength range and sensitivity of the infrared spectrum, the unique spectral characteristics of the asphalt sample are accurately captured, the ability to identify the asphalt components is enhanced, and the capture of the emission spectrum and the absorption spectrum is more accurate. Through refined adjustments, the noise of the spectral data is effectively processed, increasing the reliability of the test data. Marking key spectral peaks and comparing them with historical standard spectra provides an intuitive comparison benchmark for asphalt quality, which not only speeds up the detection process, but also improves the accuracy of the test results. Normalization processing further unifies data standards so that asphalt samples from different batches or conditions can be compared on the same benchmark, ensuring the consistency of the results and the objectivity of the comparison. The fast and accurate asphalt quality detection method greatly improves the compliance of road construction materials and the safety of roads, ensures that the materials used strictly meet engineering requirements, and has a significant impact on accelerating project progress and reducing maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0050] Figure 2 This is a detailed flow chart of S1 of the present invention;

[0051] Figure 3 This is a detailed flow chart of S2 of the present invention;

[0052] Figure 4 This is a detailed flow chart of S3 of the present invention;

[0053] Figure 5 This is a detailed flow chart of S4 of the present invention;

[0054] Figure 6 This is a detailed flow chart of S5 of the present invention;

[0055] Figure 7 This is a detailed flow chart of S6 of the present invention;

[0056] Figure 8 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0058] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0059] Embodiment 1

[0060] See also Figure 1 The present invention provides a technical solution, a method for rapid detection of asphalt quality based on infrared spectroscopy, comprising the following steps:

[0061] S1: Based on the asphalt sample, set the wavelength range and sensitivity of the infrared spectrum, identify the emission spectrum and absorption spectrum of the asphalt sample, and generate initialization spectrum data by processing signals and noise to match the technical requirements for rapid detection of asphalt quality;

[0062] S2: Based on the initialized spectral data, quickly detect the asphalt sample, identify the preset infrared spectral features, mark the key infrared spectral peaks, perform quantitative analysis on the marked peak data, and compare with the historical standard spectrum to obtain the characteristic spectrum diagram;

[0063] S3: performing normalization on the characteristic spectrum, adjusting the ratio and standardizing the intensity of the spectrum peak, checking the consistency of the data, and calculating the normalized index after processing to obtain normalized spectrum data;

[0064] S4: Use normalized spectral data to quickly test the quality of asphalt, analyze the fit spectral fingerprint of differentiated asphalt, compare the asphalt type with known standards, verify the asphalt type, and output the asphalt type classification table;

[0065] S5: Use the asphalt type classification table to analyze the chemical composition of asphalt, evaluate the performance indicators of asphalt, refer to the stability of components and the consistency of performance, and combine multiple test data to obtain the quality assessment results;

[0066] S6: Based on the quality assessment results, combined with the real-time application scenarios of asphalt, referring to the material cost, labor and time cost, and the frequency of asphalt maintenance, analyze the expected service life and maintenance cost of differentiated asphalt, and generate an asphalt adjustment plan.

[0067] The initialized spectral data include emission peak position, absorption band width and absorption peak intensity. The characteristic spectrum includes peak wavelength, peak height, peak width and matching score. The normalized spectral data includes normalized peak intensity, standardized index and consistency index. The asphalt type classification table includes type name, spectral characteristics and fitting rating. The quality assessment results include component content ratio, performance consistency rating and stability score. The asphalt adjustment plan includes service life prediction, maintenance cycle and cost-benefit analysis.

[0068] See also Figure 2 , Based on the asphalt sample, the wavelength range and sensitivity of the infrared spectrum are set, the emission spectrum and absorption spectrum of the asphalt sample are identified, and the steps of generating the initialization spectrum data by processing the signal and noise are as follows:

[0069] S101: Based on the asphalt sample, according to the control panel of the infrared spectrometer, the wavelength range is set, the sensitivity is adjusted to match the absorption band and emission peak of the asphalt, and the execution process of generating the set parameter spectrum by configuring the parameters of the infrared spectrum is as follows;

[0070] Substep S101 uses the control panel of the infrared spectrometer to set the wavelength range and sensitivity based on the asphalt sample to match the absorption band and emission peak of the asphalt. According to the characteristics of the asphalt sample, an appropriate wavelength range is selected to ensure that the main absorption band of the asphalt is covered. The sensitivity of the spectrometer is adjusted to accurately detect the emission peak of the asphalt sample. By configuring the parameters, a spectrum with set parameters is generated. The formula used is:

[0071]

[0072] Among them, P set represents the set parameter spectrum, W i is the i-th wavelength range, S i is the sensitivity of the wavelength range and n is the total number of wavelength ranges.

[0073] S102: Using the set parameter spectrum and the automatic signal optimization function of the infrared spectrometer, the spectrum signal is adjusted, the background noise is reduced, and the quality of the spectrum data is optimized. The execution process of obtaining the optimized original spectrum is as follows;

[0074] Substep S102 uses the set parameter spectrum and the automatic signal optimization function of the infrared spectrometer to adjust the spectrum signal and reduce the background noise. The automatic signal optimization function of the infrared spectrometer is started to adjust the collected spectrum signal to ensure the stability and accuracy of the signal. The noise reduction algorithm is used to reduce the background noise in the spectrum data and improve the signal quality and resolution. Through the optimization operation, the optimized original spectrum is obtained, and the formula is:

[0075]

[0076] Among them, S opt represents the original spectrum after optimization, R j is the jth collected spectral signal, N j is the background noise, and m is the total number of collected signals.

[0077] S103: According to the optimized original spectrum, the spectrum data is imported, and the format and resolution of the data are adjusted to match the technical requirements of the rapid detection of asphalt quality. The execution process of generating the initialization spectrum data is as follows;

[0078] In sub-step S103, based on the optimized original spectrum, the spectral data is imported into the data processing system, the format and resolution of the data are adjusted to match the technical requirements of the rapid detection of asphalt quality, the optimized spectral data is imported from the spectrometer into the data processing system, the data format is converted to ensure that the data format meets the standards of rapid detection, the resolution of the spectral data is adjusted to accurately reflect the quality characteristics of the asphalt sample, and the initialization spectral data is generated. The formula used is:

[0079]

[0080] Among them, D init Indicates the initialization of spectral data, F k is the format of the kth spectral data point, R k is the resolution of the data points and p is the total number of data points.

[0081] See also Figure 3 ,According to the initialized spectral data, the asphalt sample is quickly tested, the preset infrared spectral features are identified, the key infrared spectral peaks are marked, and the historical standard spectra are compared to obtain the characteristic spectrum graph. The specific steps are as follows:

[0082] S201: using the initialization spectrum data, performing an initialization scan of asphalt quality, automatically detecting and marking preset infrared spectrum features, identifying target absorption peaks and emission peaks of the infrared spectrum, identifying characteristic peaks, and generating marked characteristic spectra. The execution process is as follows;

[0083] Sub-step S201 uses the initialization spectrum data to perform an initialization scan of asphalt quality, and uses spectrum analysis software to scan the initialization spectrum data, automatically detect and mark the preset infrared spectrum features. The features include target absorption peaks and emission peaks, which are matched and identified through a preset feature database, and all identified feature peaks are recorded and marked to generate a marked feature spectrum, using the formula:

[0084]

[0085] Among them, P m represents the characteristic spectrum, α i is the label value of the i-th characteristic peak, T i is the wavelength and intensity of the characteristic peak, and n is the total number of characteristic peaks.

[0086] S202: by marking characteristic spectra, iteratively analyzing the intensity and wavelength of each marked peak, quantitatively analyzing the data of the marked peak, calculating the peak parameters, and obtaining the quantitative characteristic spectrum. The execution process is as follows;

[0087] Substep S202 iteratively analyzes the intensity and wavelength of each marked peak by marking the characteristic spectrum, performs quantitative analysis on each peak in the marked characteristic spectrum, and calculates peak parameters such as peak height, peak width, and position. Through the iterative analysis method, the parameters of each peak are accurately quantified to ensure the accuracy of the data and obtain the quantitative characteristic spectrum. The formula used is:

[0088]

[0089] Among them, Q f represents the quantitative characteristic spectrum, β j is the quantization parameter of the jth peak, F j are the intensity and wavelength of the peak, and m is the total number of labeled peaks.

[0090] S203: Based on the quantitative characteristic spectrum, compare the historical standard spectrum, analyze the fit with the known sample, sort the fit scores, identify the type and quality of the asphalt, and generate the characteristic spectrum. The execution process is as follows;

[0091] Sub-step S203 compares the quantitative characteristic spectrum with the historical standard spectrum, compares the quantitative characteristic spectrum with the standard spectrum of the known sample, and analyzes the fit of each spectrum. The similarity between the quantitative characteristic spectrum and the standard spectrum is calculated by the fit scoring method, and the fit scores are sorted. According to the scoring results, the type and quality of asphalt are identified, and the characteristic spectrum is generated. The formula used is:

[0092]

[0093] Among them, Fs Indicates the fitting score of the characteristic spectrum, Q k is the kth characteristic spectrum point, S k is the corresponding standard spectral point, and p is the total number of spectral points.

[0094] See also Figure 4 , perform normalization on the characteristic spectrum, adjust the ratio and standardize the intensity of the spectrum peak, check the consistency of the data, and calculate the normalized index after processing. The specific steps for obtaining normalized spectrum data are:

[0095] S301: Using the characteristic spectrum graph, adjusting the ratio of the infrared spectrum peaks, proportionalizing the intensity of each peak with the intensity of the standard spectrum, checking that the infrared spectrum data is scaled at the same ratio, and generating the proportionally adjusted spectrum. The execution process is as follows;

[0096] Substep S301 uses the characteristic spectrum to adjust the ratio of the infrared spectrum peaks, and scales the intensity of each peak with the intensity of the standard spectrum according to the characteristic spectrum. By calculating the scale factor of each peak, it is ensured that all infrared spectrum data are scaled at the same ratio. Through adjustment, a unified benchmark is provided for subsequent standardization processing, and a scaled spectrum is generated, using the formula:

[0097]

[0098] Among them, P adj represents the proportional adjustment spectrum, I i,exp is the intensity of the ith experimental spectrum peak, I i,std is the intensity of the corresponding peak in the standard spectrum, and n is the total number of peaks.

[0099] S302: Based on the ratio-adjusted spectrum, standardization processing is performed to convert the adjusted spectrum data into a standardized score, and the scale of the data is adjusted by calculating the standard deviation and the mean to obtain the standardized spectrum data. The execution process is as follows;

[0100] The sub-step S302 adjusts the spectrum based on the ratio, performs standardization processing, converts the adjusted spectrum data into a standardized score, and adjusts the data to a standardized scale by calculating the standard deviation and mean of each data point to ensure the uniformity and comparability of the data, provides standardized data input for subsequent consistency testing and normalization, and generates standardized spectrum data. The formula used is:

[0101]

[0102] Among them, Z i represents the standardized score of the ith data point, P adj,iis the scaled spectral data point, μ is the mean of the data, and σ is the standard deviation of the data.

[0103] S303: Perform consistency check and normalization index calculation through standardized spectral data, verify that the standardized spectral data matches the asphalt quality standard, calculate the normalization index to evaluate the statistical consistency of the data, and generate the execution process of normalized spectral data as follows;

[0104] Sub-step S303 performs consistency check and normalization index calculation by standardizing spectral data, and uses standardized spectral data to perform consistency check to verify whether the data meets the asphalt quality standard. Calculate the normalization index, evaluate the statistical consistency of the data, ensure the reliability and accuracy of the data, and generate normalized spectral data using the formula:

[0105]

[0106] Among them, N i represents the normalized index of the ith data point, Z i is the normalized spectral data point, Z min and Z max are the minimum and maximum values ​​of the normalized data, respectively.

[0107] See also Figure 5 , using normalized spectral data, quickly detect asphalt quality, analyze the fit spectral fingerprint of differentiated asphalt, compare asphalt type with known standards, verify asphalt type, and output asphalt type classification table. The specific steps are:

[0108] S401: Using normalized spectral data and a spectral matching algorithm, a rapid test of asphalt quality is performed, and the execution process of generating a fit analysis result by comparing the spectral data with a fingerprint in a preset spectrum is as follows;

[0109] Sub-step S401 uses normalized spectral data and a spectral matching algorithm to quickly detect asphalt quality. The normalized spectral data is compared with the fingerprint area in the standard spectrum through a preset spectral matching algorithm. The degree of fit between the sample spectrum and the standard spectrum is evaluated by calculating the matching degree of each data point, and all comparison results are recorded to generate a fit analysis result.

[0110] The formula of the spectral matching algorithm is as follows:

[0111]

[0112] Among them, S is the weighted average fitness score, R i is the spectral value in the normalized spectral data, T i is the target spectrum value in the preset spectrum, n is the total number of data points, wi is the weight coefficient for each data point.

[0113] The execution flow is as follows:

[0114] Determine the importance of each spectral data point i and its sensitivity to the fit, and assign a weight w to each data point based on its variability and the importance of the chemical identity i The value of the weight coefficient is determined by statistical analysis, which involves analyzing the degree of variation of each point in the historical data set and its influence on the result. The measured spectral value R of each data point is calculated by weighted square difference. i With the target spectrum value T i The difference is squared and multiplied by the corresponding weight w i , sum all weighted square differences and divide them by the total number of data points n to obtain the weighted average fit score S. A smaller score indicates a higher fit, and vice versa, a lower fit, generating a weighted fit analysis result.

[0115] S402: According to the fit analysis results, compare the asphalt type, match the sample spectrum with the known standard spectrum, analyze the best matching type of the asphalt sample, identify the spectral characteristics of the differentiated asphalt type, and obtain the asphalt type verification result. The execution process is as follows;

[0116] In sub-step S402, based on the fit analysis results, the asphalt type is compared, the sample spectrum is matched with the known standard spectrum one by one, and the optimal matching type of the sample spectrum and the standard spectrum is identified. By analyzing the spectral features of each matching type, the differentiated asphalt type is identified, and the spectral features of each matching type are recorded to obtain the asphalt type verification result.

[0117] S403: Based on the asphalt type verification result, the type, matching degree and associated spectral characteristics of the detected asphalt sample are identified, and the execution process of outputting the asphalt type classification table by integrating and formatting the asphalt quality associated data is as follows;

[0118] Substep S403 identifies the type, matching degree and associated spectral characteristics of the detected asphalt sample based on the asphalt type verification result, integrates and formats all asphalt quality related data, analyzes the sample type, matching degree and spectral characteristics through analysis and sorting of all test results, provides detailed asphalt quality information to users, and outputs an asphalt type classification table using the formula:

[0119]

[0120] Among them, C class Represents the asphalt type classification table, is the kth asphalt type, is the matching degree, is the spectral characteristic and p is the total number of asphalt types.

[0121] See also Figure 6 , using the asphalt type classification table, by analyzing the chemical composition of asphalt, quickly testing the quality of asphalt, evaluating the performance indicators of asphalt, referring to the stability of the composition and the consistency of the performance, the specific steps to obtain the quality assessment results are as follows:

[0122] S501: According to the asphalt type classification table, quickly identify and record the key chemical components of each asphalt type, analyze the deviation of asphalt quality and the cause of the deviation by comparing with the historical chemical composition standards, and generate the chemical composition analysis results. The execution process is as follows;

[0123] Substep S501 quickly identifies and records the key chemical components of each asphalt type based on the asphalt type classification table. Through the differentiated classification of asphalt types, the chemical composition data corresponding to each asphalt type is extracted, and the data is compared with the historical chemical composition standards to analyze the deviation of asphalt quality. The reasons for the deviation will be further analyzed, such as changes in raw materials or production process problems, and the chemical composition analysis results will be generated using the formula:

[0124]

[0125] Among them, C diff Indicates the percentage deviation of chemical composition, C i,cur is the current chemical composition content of the i-th asphalt type, C i,sta is the corresponding historical standard content, and n is the total number of chemical components.

[0126] S502: Based on the chemical composition analysis results, the performance indicators of each asphalt are evaluated, the component stability and performance consistency are calculated, the quality fluctuation of the differentiated components and the impact on the asphalt performance are identified, and the execution process of obtaining the performance stability evaluation results is as follows;

[0127] Sub-step S502 evaluates the performance indicators of each asphalt based on the results of chemical composition analysis, and calculates the composition stability and performance consistency of each asphalt based on the results of chemical composition analysis. By analyzing the fluctuation of chemical composition, the components that have a greater impact on asphalt performance are identified, and the specific impact on asphalt performance is evaluated to obtain the performance stability evaluation result, using the formula:

[0128]

[0129] Among them, P stab represents the performance stability evaluation result, C j,dev is the deviation C of the jth component j,standard is the corresponding standard content and m is the total number of components evaluated.

[0130] S503: Using the performance stability evaluation results, evaluate the asphalt quality, combine multiple test data, analyze the overall quality status and recommended use range of the asphalt, and output the quality evaluation results. The execution process is as follows;

[0131] Substep S503 uses the performance stability evaluation results to evaluate the asphalt quality and combines multiple test data, such as physical tests, durability tests, etc. to comprehensively analyze the overall quality of asphalt. Based on multiple test data, the recommended use range of asphalt is determined to ensure the best performance in different application scenarios, provide users with comprehensive asphalt quality information and usage recommendations, and output quality evaluation results using the formula:

[0132]

[0133] Among them, Q eval represents the quality assessment result, W k is the weight of the k-th test data, T k is the corresponding test result, and p is the total number of test data.

[0134] See also Figure 7 , based on the quality assessment results, combined with the real-time application scenarios of asphalt, the expected service life, maintenance cost and replacement frequency of differentiated asphalt are analyzed, and the steps for generating asphalt adjustment plans are as follows:

[0135] S601: Based on the quality assessment results, by analyzing the trends of historical data and performance indicators, the service life of the differentiated asphalt samples is simulated and the expected service life is calculated. The execution process of generating the expected service life analysis results is as follows;

[0136] Based on the quality assessment results, sub-step S601 simulates the service life of differentiated asphalt samples by analyzing the trends of historical data and performance indicators. It analyzes the trends of performance indicators in historical data and quality assessment results, and simulates the service life of each type of asphalt sample according to the trends and known usage conditions. Through model prediction and calculation, the expected service life of each asphalt sample is obtained, and the expected service life analysis results are generated, using the formula:

[0137]

[0138] Among them, L e represents the expected service life, P i,quality is the quality assessment score of the i-th asphalt, T i is the performance index trend value of asphalt, D i is the durability coefficient of asphalt in historical data, and n is the total number of asphalt samples.

[0139] S602: Using the expected service life analysis results, calculate the asphalt maintenance cost and replacement frequency, refer to the material cost, labor and time cost, and the frequency of asphalt maintenance, and obtain the maintenance and replacement cost analysis results. The execution process is as follows;

[0140] Sub-step S602 uses the expected service life analysis results to calculate the asphalt maintenance cost and replacement frequency. By predicting and analyzing the service life of asphalt, the maintenance cost and replacement frequency of each asphalt are calculated. With reference to material cost, labor cost, time cost and maintenance frequency, the maintenance and replacement cost of each asphalt is comprehensively evaluated to generate the maintenance and replacement cost analysis results. The formula used is:

[0141]

[0142] Among them, C m represents the maintenance and replacement cost, M j is the material cost, L j is the labor cost, T j is the time cost, F j is the maintenance frequency, and m is the total number of different maintenance items.

[0143] S603: Based on the maintenance and replacement cost analysis results, and referring to the application scenarios and environment of asphalt, formulate a targeted asphalt adjustment plan, including the amount of asphalt used, maintenance plan and preventive measures, and output the execution process of the asphalt adjustment plan as follows;

[0144] Sub-step S603 formulates a targeted asphalt adjustment plan based on the maintenance and replacement cost analysis results, and refers to the application scenarios and environment of asphalt. According to the maintenance cost and replacement efficiency of asphalt, combined with the actual application scenarios and environmental conditions of asphalt, the asphalt usage, maintenance plan and preventive measures are analyzed, and the asphalt adjustment plan is output. The formula used is:

[0145]

[0146] Among them, A s represents the asphalt adjustment scheme, U k is the usage, M k is the maintenance plan, P k It is a precautionary measure. k is the environmental condition and p is the total number of adjustment options.

[0147] See also Figure 8 , Asphalt quality rapid detection system based on infrared spectroscopy, the asphalt quality rapid detection system based on infrared spectroscopy is used to perform the above-mentioned asphalt quality rapid detection method based on infrared spectroscopy, the system includes:

[0148] The spectrum analysis module adjusts the wavelength range and sensitivity of the infrared spectrum based on the asphalt sample, captures the emission and absorption spectra of the asphalt, processes the original signal to avoid noise interference, and generates original spectrum data;

[0149] The feature recognition module uses the original spectral data to analyze and mark key infrared spectral features, identify absorption and emission peaks, and compare them with historical spectral features to obtain a feature spectrum graph;

[0150] The normalization processing module uses the characteristic spectrum to perform intensity ratio adjustment and normalization processing of the spectrum peak, checks data consistency, and calculates the normalization index to obtain normalized spectrum data;

[0151] The quality analysis module conducts rapid detection of asphalt quality based on normalized spectral data, analyzes the spectral fingerprint of the fit of differentiated asphalt, compares and verifies the asphalt type, and outputs an asphalt type classification table;

[0152] The strategy adjustment module is based on the asphalt type classification table and combines the real-time application scenarios of asphalt to analyze the expected service life, maintenance cost and replacement frequency, evaluate the performance indicators and component stability of asphalt, and generate an asphalt adjustment plan.

[0153] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A rapid asphalt quality detection method based on infrared spectroscopy, characterized in that: The following steps are involved: Based on the asphalt sample, the wavelength range and sensitivity of the infrared spectrum are set, the emission spectrum and absorption spectrum of the asphalt sample are identified, and the initialization spectrum data is generated by processing the signal and noise; According to the initialized spectral data, the asphalt sample is quickly tested, the preset infrared spectral features are identified, the key infrared spectral peaks are marked, and the historical standard spectra are compared to obtain a characteristic spectrum diagram; Normalizing the characteristic spectrum, proportionally adjusting and normalizing the intensity of the spectrum peak, checking the consistency of the data, and calculating the normalized index after processing to obtain normalized spectrum data; Using the normalized spectral data, quickly test the quality of asphalt, analyze the fit spectral fingerprint of differentiated asphalt, compare the asphalt type with known standards, verify the asphalt type, and output an asphalt type classification table; By using the asphalt type classification table, the asphalt quality is quickly tested by analyzing the chemical composition of the asphalt, the performance index of the asphalt is evaluated, and the quality evaluation result is obtained by referring to the component stability and performance consistency; Based on the quality assessment results and combined with the real-time application scenarios of asphalt, the expected service life, maintenance cost and replacement frequency of differentiated asphalt are analyzed to generate an asphalt adjustment plan.

2. The method for rapid detection of asphalt quality based on infrared spectroscopy according to claim 1 is characterized in that: The initialized spectral data includes the emission peak position, absorption band width and absorption peak intensity, the characteristic spectrum diagram includes the peak wavelength, peak height, peak width and matching score, the normalized spectral data includes the normalized peak intensity, standardization index and consistency index, the asphalt type classification table includes the type name, spectral characteristics and fitting rating, the quality assessment results include the component content ratio, performance consistency rating and stability score, and the asphalt adjustment plan includes service life prediction, maintenance cycle and cost-benefit analysis.

3. The method for rapid detection of asphalt quality based on infrared spectroscopy according to claim 1 is characterized in that: Based on the asphalt sample, the wavelength range and sensitivity of the infrared spectrum are set, the emission spectrum and absorption spectrum of the asphalt sample are identified, and the steps of generating the initialization spectrum data by processing the signal and noise are as follows: Based on the asphalt sample, according to the control panel of the infrared spectrometer, the wavelength range is set, the sensitivity is adjusted to match the absorption band and emission peak of the asphalt, and the parameters of the infrared spectrum are configured to generate the set parameter spectrum; Using the set parameter spectrum and the automatic signal optimization function of the infrared spectrometer, the spectral signal is adjusted, the background noise is reduced, the quality of the spectral data is optimized, and an optimized original spectrum is obtained; According to the optimized original spectrum, the spectral data is imported, and the format and resolution of the data are adjusted to match the technical requirements for rapid detection of asphalt quality, thereby generating initialization spectral data.

4. The method for rapid detection of asphalt quality based on infrared spectroscopy according to claim 1 is characterized in that: According to the initialized spectral data, the asphalt sample is quickly detected, the preset infrared spectral features are identified, the key infrared spectral peaks are marked, and the historical standard spectra are compared to obtain the characteristic spectrum diagram in the following steps: The initialized spectral data is used to perform an initialization scan of asphalt quality, automatically detect and mark preset infrared spectral features, identify target absorption peaks and emission peaks of the infrared spectrum, identify characteristic peaks, and generate a marked characteristic spectrum; By using the marker characteristic spectrum, the intensity and wavelength of each marker peak are iteratively analyzed, the data of the marker peak is quantitatively analyzed, the peak parameters are calculated, and a quantitative characteristic spectrum is obtained; Based on the quantitative characteristic spectrum, the historical standard spectrum is compared, the fit with the known samples is analyzed, and the fit scores are ranked to identify the type and quality of the asphalt and generate a characteristic spectrum graph.

5. The method for rapid detection of asphalt quality based on infrared spectroscopy according to claim 1 is characterized in that: The characteristic spectrum is normalized, the intensity of the spectrum peak is proportionally adjusted and standardized, the consistency of the data is checked, and the normalized index after processing is calculated. The specific steps of obtaining normalized spectrum data are: Using the characteristic spectrum, adjusting the ratio of the infrared spectrum peaks, proportionalizing the intensity of each peak with the intensity of the standard spectrum, checking that the infrared spectrum data is scaled at the same ratio, and generating a proportionally adjusted spectrum; Adjusting the spectrum based on the ratio, performing a standardization process, converting the adjusted spectrum data into a standardized score, and adjusting the scale of the data by calculating a standard deviation and a mean to obtain standardized spectrum data; The standardized spectral data are used to perform consistency checks and normalized index calculations to verify that the standardized spectral data matches the asphalt quality standard, calculate the normalized index to evaluate the statistical consistency of the data, and generate normalized spectral data.

6. The method for rapid detection of asphalt quality based on infrared spectroscopy according to claim 1 is characterized in that: The steps of using the normalized spectral data to quickly detect the quality of asphalt, analyze the fit spectral fingerprint of differentiated asphalt, compare the asphalt type with the known standard, verify the asphalt type, and output the asphalt type classification table are as follows: Using the normalized spectral data and a spectral matching algorithm, a rapid detection of asphalt quality is performed, and a fit analysis result is generated by comparing the spectral data with a fingerprint in a preset spectrum; According to the fit analysis results, the asphalt type is compared, the sample spectrum is matched with the known standard spectrum, the optimal matching type of the asphalt sample is analyzed, the spectral characteristics of the differentiated asphalt type are identified, and the asphalt type verification result is obtained; Based on the asphalt type verification results, the type, matching degree and associated spectral characteristics of the detected asphalt sample are identified, and an asphalt type classification table is output by integrating and formatting asphalt quality related data.

7. The method for rapid detection of asphalt quality based on infrared spectroscopy according to claim 6 is characterized in that: The formula of the spectrum matching algorithm is as follows: Among them, S is the weighted average fitness score, R i is the spectral value in the normalized spectral data, T i is the target spectrum value in the preset spectrum, n is the total number of data points, w i is the weight coefficient for each data point.

8. The method for rapid detection of asphalt quality based on infrared spectroscopy according to claim 1 is characterized in that: The steps of using the asphalt type classification table to quickly detect the quality of asphalt by analyzing the chemical composition of asphalt, evaluating the performance indicators of asphalt, and obtaining the quality evaluation results by referring to the component stability and performance consistency are as follows: According to the asphalt type classification table, the key chemical components of each asphalt type are quickly identified and recorded, and by comparing with historical chemical composition standards, the deviation of asphalt quality and the cause of the deviation are analyzed to generate chemical composition analysis results; Based on the chemical composition analysis results, the performance indicators of each asphalt are evaluated, the component stability and performance consistency are calculated, the quality fluctuations of the differentiated components and their impact on the asphalt performance are identified, and the performance stability evaluation results are obtained; The performance stability evaluation results are used to evaluate the quality of asphalt, and the overall quality status and recommended use range of asphalt are analyzed in combination with multiple test data to output quality evaluation results.

9. The method for rapid detection of asphalt quality based on infrared spectroscopy according to claim 1, characterized in that: According to the quality assessment results, combined with the real-time application scenarios of asphalt, the expected service life, maintenance cost and replacement frequency of differentiated asphalt are analyzed, and the steps for generating asphalt adjustment plans are as follows: Based on the quality assessment results, by analyzing the trends of historical data and performance indicators, service life simulation is performed on differentiated types of asphalt samples, expected service life is calculated, and expected service life analysis results are generated; The expected service life analysis results are used to calculate the asphalt maintenance cost and replacement frequency, and the maintenance and replacement cost analysis results are obtained by referring to the material cost, labor and time cost, and the frequency of asphalt maintenance; Based on the maintenance and replacement cost analysis results, and referring to the application scenarios and environment of asphalt, a targeted asphalt adjustment plan is formulated, including the amount of asphalt used, maintenance plan and preventive measures, and the asphalt adjustment plan is output.

10. Rapid asphalt quality detection system based on infrared spectroscopy, characterized in that: According to any one of claims 1 to 9, the method for rapid detection of asphalt quality based on infrared spectroscopy comprises: The spectrum analysis module adjusts the wavelength range and sensitivity of the infrared spectrum based on the asphalt sample, captures the emission and absorption spectra of the asphalt, processes the original signal to avoid noise interference, and generates original spectrum data; The feature recognition module uses the original spectral data to analyze and mark key infrared spectral features, identify absorption and emission peaks, and compare them with historical spectral features to obtain a feature spectrum graph; The normalization processing module uses the characteristic spectrum graph to perform intensity ratio adjustment and normalization processing of the spectrum peak, check data consistency, and calculate the normalization index to obtain normalized spectrum data; The quality analysis module performs rapid detection of asphalt quality based on the normalized spectral data, analyzes the fit spectral fingerprint of the differentiated asphalt, compares and verifies the asphalt type, and outputs an asphalt type classification table; The strategy adjustment module is based on the asphalt type classification table and combines the real-time application scenarios of asphalt to analyze the expected service life, maintenance cost and replacement frequency, evaluate the performance indicators and component stability of asphalt, and generate an asphalt adjustment plan.

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