An asphalt detection method, device, storage medium and electronic equipment

Abstract

To solve the problem that it is difficult to distinguish the regenerated components and new components in mixed asphalt, a kind of asphalt detection method and device, storage medium and electronic equipment are provided, which are related to the technical field of material testing. The method comprises the following steps: obtaining spectral information of mixed asphalt, the mixed asphalt at least comprising first asphalt and second asphalt; the aging degree of the first asphalt is greater than that of the second asphalt; analyzing the spectral information by a target model to obtain brand information, proportion information and aging degree information of the first asphalt and the second asphalt; determining the detection result of the mixed asphalt based on the brand information, the proportion information and the aging degree information of the first asphalt and the second asphalt; wherein the target model is constructed based on reference spectral data of asphalts of multiple brands and aging trajectory functions of asphalts of multiple brands. By using the present application, the asphalt components of different aging degrees in the mixed asphalt can be identified at the same time.

Description

Technical Field

[0001] This application relates to the field of materials testing technology, and in particular to a method, apparatus, storage medium and electronic equipment for testing asphalt. Background Technology

[0002] In the process of road maintenance and recycling, a large amount of aged reclaimed asphalt (RAP) is mixed with new asphalt. However, the asphalt testing methods in related technologies are mainly based on the identification of pure asphalt brand characteristics, which makes it difficult to accurately distinguish between the recycled components and the new components in the mixed asphalt. This makes it impossible to effectively monitor the amount of recycled material added and the degree of aging, affecting the quality control and durability assessment of road engineering. Summary of the Invention

[0003] In view of this, this application provides a method, apparatus, storage medium and electronic device for asphalt testing to overcome the shortcomings of the prior art.

[0004] According to the first aspect of this application, a method for testing asphalt is provided, comprising:

[0005] Obtain spectral information of the mixed asphalt, which includes at least a first asphalt and a second asphalt; the aging degree of the first asphalt is greater than that of the second asphalt.

[0006] The spectral information is analyzed using the target model to obtain the brand information, proportion information, and aging degree information of the first and second asphalt.

[0007] Based on the brand information, ratio information, and aging degree information of the first and second asphalt, the test results of the mixed asphalt are determined.

[0008] The target model is constructed based on a first database and a second database. The first database includes benchmark spectral data of asphalt from multiple brands, and the second database includes aging trajectory functions of asphalt from multiple brands. The aging trajectory functions characterize the evolution of the aging degree of asphalt from multiple brands under different aging time parameters.

[0009] Another aspect of this application provides an asphalt testing device, comprising:

[0010] The acquisition module is used to acquire the spectral information of the mixed asphalt, which includes at least a first asphalt and a second asphalt; the aging degree of the first asphalt is greater than that of the second asphalt.

[0011] The analysis module is used to analyze spectral information through the target model to obtain brand information, ratio information, and aging degree information of the first and second asphalt.

[0012] The determination module is used to determine the test results of the mixed asphalt based on the brand information, proportion information, and aging degree information of the first and second asphalt.

[0013] The target model is constructed based on a first database and a second database. The first database includes benchmark spectral data of asphalt from multiple brands, and the second database includes aging trajectory functions of asphalt from multiple brands. The aging trajectory functions characterize the evolution of the aging degree of asphalt from multiple brands under different aging time parameters.

[0014] Another aspect of this application provides an electronic device comprising:

[0015] One or more processors;

[0016] Memory, used to store one or more programs.

[0017] When the one or more programs are executed by the one or more processors, the one or more processors implement the method described above.

[0018] Another aspect of this application provides a computer-readable storage medium storing computer-executable instructions that, when executed, are used to implement the method described above.

[0019] By adopting the technical solution of this application, a target model can be constructed based on a first database containing reference spectral data and a second database containing aging trajectory functions. This allows the target model to simultaneously identify asphalt components with different aging degrees in the mixed asphalt, overcoming the technical deficiency of related technologies that rely solely on a pure asphalt brand feature database for identification and cannot distinguish between recycled and virgin components. The aging trajectory function characterizes the evolution of the aging degree of multiple brands of asphalt under different aging time parameters, enabling the target model to accurately analyze the spectral information of the mixed asphalt and obtain the brand information, proportion information, and aging degree information of both the first and second asphalt. This allows for effective monitoring of the amount of recycled material added and the degree of aging, providing accurate detection results for quality control and durability assessment in road engineering.

[0020] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent from the following description. Attached Figure Description

[0021] The above and other objects, features and advantages of this application will become clearer from the following description of embodiments with reference to the accompanying drawings, in which:

[0022] Figure 1A flowchart illustrating an asphalt testing method provided in this application is shown schematically.

[0023] Figure 2 This schematic diagram illustrates the structural block diagram of an asphalt testing device provided in this application;

[0024] Figure 3 A schematic block diagram of an electronic device provided in this application is shown.

[0025] Explanation of reference numerals in the attached figures: 301, processor; 302, ROM; 303, RAM; 304, bus; 305, I / O interface; 306, input section; 307, output section; 308, storage section; 309, communication section; 310, driver; 311, removable medium. Detailed Implementation

[0026] The embodiments of this application will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of this application. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of this application for ease of explanation. However, it will be apparent that one or more embodiments may be implemented without these specific details. Furthermore, descriptions of well-known structures and technologies are omitted in the following description to avoid unnecessarily obscuring the concepts of this application.

[0027] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of this application. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0028] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0029] When using expressions such as "at least one of A, B and C", they should generally be interpreted in accordance with the meaning that is commonly understood by those skilled in the art (e.g., "a system having at least one of A, B and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B and C, etc.).

[0030] Figure 1 A flowchart illustrating an asphalt testing method provided in an embodiment of this application is shown.

[0031] like Figure 1 As shown, the asphalt testing method includes steps S101 to S103.

[0032] Step S101: Obtain the spectral information of the mixed asphalt, wherein the mixed asphalt includes at least a first asphalt and a second asphalt; the aging degree of the first asphalt is greater than that of the second asphalt.

[0033] Step S102: Analyze the spectral information using the target model to obtain brand information, proportion information, and aging degree information of the first and second asphalts; wherein, the target model is constructed based on the first and second databases; the first database includes benchmark spectral data of asphalts from multiple brands, and the second database includes aging trajectory functions of asphalts from multiple brands; the aging trajectory function characterizes the evolution of the aging degree of asphalts from multiple brands under different aging time parameters.

[0034] Step S103: Based on the brand information, proportion information, and aging degree information of the first and second asphalt, determine the test results of the mixed asphalt.

[0035] In step S101, mixed asphalt refers to a composite asphalt material made by mixing two or more asphalt materials with different aging degrees in a certain proportion. In the embodiments of this application, it can be understood as an asphalt mixture used in the process of road maintenance and recycling, for road paving, repair or recycling projects.

[0036] The mixed asphalt includes at least a first asphalt and a second asphalt. The first asphalt refers to the asphalt component in the mixed asphalt with a higher degree of aging. In the embodiments of this application, it can be understood as asphalt material whose performance has deteriorated due to use, thermal oxidation, ultraviolet aging or traffic load, and is used as a recycled material to achieve material recycling.

[0037] Similarly, the second bitumen refers to the bitumen component in the mixed bitumen with a lower degree of aging. In the embodiments of this application, it can be understood as newly prepared or slightly aged bitumen material used to provide bonding and workability in the mixed bitumen.

[0038] It should be noted that the difference in aging degree between the first and second asphalt is a relative difference. Compared with the second asphalt, the first asphalt has undergone more significant aging changes in chemical composition and physical properties, including increased volatility of lightweight components, greater changes in the ratio of resins to asphaltenes, a more significant decrease in penetration, and a more significant increase in softening point. This difference in aging degree causes the two asphalts to exhibit different absorption peak positions and intensities in their infrared spectral characteristics, providing a technical basis for the spectral identification and quantitative analysis of this application.

[0039] In one feasible implementation, the spectral information of the mixed asphalt can be obtained using an infrared spectrometer. A sample of the mixed asphalt is prepared into a thin film of appropriate thickness and scanned within an infrared spectrometer to obtain infrared absorption spectral data of the mixed asphalt within a preset wavenumber range. This spectral data reflects the molecular vibrational characteristics of each component in the mixed asphalt.

[0040] In another feasible implementation, Raman spectroscopy can be used to obtain the spectral information of the mixed asphalt. The mixed asphalt sample is prepared into a uniform thin layer, and Raman scattering is generated by laser excitation. The frequency change of the scattered light is detected to obtain Raman spectral data reflecting the molecular structure information of the mixed asphalt.

[0041] In step S102, the target model refers to a mathematical model used to decompose the spectral information of mixed asphalt into asphalt components of different brands and different aging degrees.

[0042] The target model is constructed based on the first database and the second database. The first database refers to a reference dataset containing the standard spectral characteristics of asphalt of multiple brands in the new material state. In this embodiment of the application, it can be understood as the benchmark spectral feature library of each brand of asphalt, which is used to provide the initial spectral identification benchmark and brand discrimination basis for different brands of asphalt.

[0043] Similarly, the second database refers to a function dataset that describes the evolution of spectral characteristics of various brands of asphalt from virgin to different aging stages. In this embodiment, it can be understood as an aging evolution trajectory database of various brands of asphalt, used to characterize the dynamic changes in spectral characteristics of asphalt during the aging process and to quantitatively assess the degree of aging.

[0044] The aging trajectory function refers to a mathematical function that describes the continuous change of spectral characteristics of a specific brand of asphalt from its baseline state to different aging degrees. In this embodiment, it can be understood as a continuous function model with aging time as a parameter and spectral characteristics as the output, used to predict the spectral characteristics of the brand of asphalt at any aging stage.

[0045] After analyzing the spectral information using the target model, the brand information, proportion information, and aging degree information of the first and second asphalts can be obtained.

[0046] Among them, brand information refers to the identity attributes of asphalt components identified by spectral feature matching, such as manufacturer identification, product model, and technical specifications. In this application embodiment, it can be understood as the asphalt brand attribution result obtained by pattern recognition based on the benchmark spectral data in the first database, which is used to determine the source manufacturer and product grade of each component in the mixed asphalt.

[0047] Similarly, the proportion information refers to the relative content or proportion of each brand of asphalt component in the mixed asphalt. In the embodiments of this application, it can be understood as the mass fraction, volume fraction or mole fraction of each component in the mixed asphalt obtained by quantitative calculation through spectral decomposition algorithm. It is used to monitor the accuracy of the mixed asphalt proportion and the uniformity of the components, and to ensure that the material mix proportion meets the design requirements and construction specifications.

[0048] Similarly, aging information refers to the quantitative evaluation results of changes in the chemical composition and physical properties of asphalt components caused by factors such as thermal oxidation, ultraviolet radiation, and mechanical action during use. In the embodiments of this application, it can be understood as the asphalt deterioration status assessment obtained by analyzing the aging trajectory function in the second database, which is used to determine the degree of performance degradation and remaining service life of asphalt materials.

[0049] In one feasible implementation, a spectral decomposition algorithm can be used to construct the target model. A brand spectral feature matrix is ​​established based on a first database, and an aging trajectory function library is established based on a second database. Through mathematical methods such as sparse representation, nonnegative matrix factorization, or independent component analysis, the spectral information of the mixed asphalt is decomposed into a linear combination of the reference spectra of each brand and the aging trajectory function, thereby obtaining information on the brand, proportion, and degree of aging of each component.

[0050] In step S103, the main brand composition of the mixed asphalt can be determined based on the brand information of the first and second asphalts, the proportion of recycled material and the proportion of new material used can be calculated based on the proportion information, the overall performance status of the mixed asphalt can be evaluated based on the aging degree information, and a test result report including brand composition, mix structure and performance grade can be formed.

[0051] In another feasible implementation, the obtained brand information, proportion information, and aging degree information can be compared and analyzed with preset quality standards and technical specifications to determine whether the mixed asphalt meets the engineering design requirements, output a qualified or unqualified test conclusion, and provide specific quality evaluation indicators and improvement suggestions.

[0052] By adopting the technical solution of this application, a target model can be constructed based on a first database containing reference spectral data and a second database containing aging trajectory functions. This allows the target model to simultaneously identify asphalt components with different aging degrees in the mixed asphalt, overcoming the technical deficiency of related technologies that rely solely on a pure asphalt brand feature database for identification and cannot distinguish between recycled and virgin components. The aging trajectory function characterizes the evolution of the aging degree of multiple brands of asphalt under different aging time parameters, enabling the target model to accurately analyze the spectral information of the mixed asphalt and obtain the brand information, proportion information, and aging degree information of both the first and second asphalt. This allows for effective monitoring of the amount of recycled material added and the degree of aging, providing accurate detection results for quality control and durability assessment in road engineering.

[0053] In practical applications, different brands of asphalt exhibit their own unique evolution patterns during the aging process. The spectral characteristics of asphalt of the same brand changing from a virgin state to a heavily aged state are not randomly distributed, but rather evolve continuously along a specific trajectory. Static brand feature databases of related technologies cannot describe this dynamic change process, leading to misjudgments when identifying asphalt of the same brand at different aging levels.

[0054] To address the aforementioned issues, as an optional embodiment based on the above-described embodiments, the aging trajectory function is constructed using the reference spectral data of asphalt from various brands in the first database through an aging evolution function. The aging evolution function characterizes the evolution of the spectral characteristics of asphalt over time. The aging trajectory function of each brand of asphalt has an aging offset direction. The aging offset direction characterizes the changing trend of the reference spectral data of asphalt from the reference spectral state to the aged spectral state.

[0055] The aging evolution function refers to a mathematical function model that describes the continuous change of spectral characteristics of a specific brand of asphalt over time during the aging process. In this embodiment, it can be understood as a nonlinear function with aging time as the input variable and the degree of change in spectral characteristics as the output variable. It is used to quantitatively describe the spectral evolution of asphalt materials from virgin state to different aging stages and to predict the spectral characteristic state at any aging time point. Through the aging evolution function, the aging process of asphalt can be extended from discrete time points to a continuous time trajectory, enabling the target model to identify and analyze asphalt components at any aging degree.

[0056] Similarly, the aging offset direction refers to the vector in the multidimensional spectral feature space that characterizes the dominant direction of spectral changes caused by aging in a specific brand of asphalt. In this embodiment, it can be understood as a spatial direction vector pointing from the reference spectral state to the aging spectral state, used to determine the main trend and change pattern of spectral characteristics of that brand of asphalt during the aging process. The aging offset direction reflects the unique aging characteristics of different brands of asphalt, enabling different brands of asphalt to be accurately distinguished even at the same degree of aging through their unique offset direction.

[0057] By adopting the technical solution of this application, the aging trajectory function constructed based on the aging evolution function can expand the static spectral feature points of each brand of asphalt into a dynamic trajectory describing the complete aging process. This overcomes the technical deficiency of related technologies that rely solely on a pure asphalt brand feature library for identification and cannot accurately identify asphalt at different aging levels. The introduction of the aging offset direction allows different brands of asphalt to be accurately distinguished by their unique change trends even at the same aging level, solving the problem of misjudgment caused by the blurring of brand characteristics due to aging.

[0058] The construction process of the aging trajectory function will be explained in detail below.

[0059] Specifically, the process of constructing the aging trajectory function may include the following steps:

[0060] Step S201: Obtain spectral data of asphalt from multiple brands under different aging time parameters;

[0061] Step S202: Based on the reference spectral data of asphalt for each brand, calculate the vector difference between the spectral data of each brand of asphalt under different aging time parameters and the corresponding reference spectral data.

[0062] Step S203: Determine the aging offset direction of the corresponding brand of asphalt based on the vector difference;

[0063] Step S204: Fit the aging degree projection values ​​of each brand of asphalt under different aging time parameters to obtain the aging evolution function corresponding to each brand of asphalt.

[0064] Step S205: Construct the mapping relationship between the baseline spectral data of each brand of asphalt and the corresponding aging offset direction and aging evolution function to obtain the aging trajectory function of each brand of asphalt.

[0065] In step S201, spectral data under different aging time parameters are obtained for asphalt samples from multiple different brands by establishing a standardized laboratory aging procedure.

[0066] Specifically, several brands of asphalt commonly found in the market were selected as research subjects. Standard aging methods such as rotating thin film oven tests and pressure aging vessel tests were used to prepare asphalt samples with gradient aging degrees under preset temperature conditions and a series of different aging time parameters. Subsequently, each aged sample was prepared into a uniform thin film, and scanned using an infrared spectrometer or a Raman spectrometer within a preset wavenumber range to obtain the spectral data of each brand of asphalt under the corresponding aging time parameters.

[0067] For example, the acquired spectral data can be represented as:

[0068] ;

[0069] In the formula, Indicate brand Aging time parameters Spectral data below; This represents the aging time parameter (k represents the time point under the realized conditions). Represents the wavenumber, i.e., the horizontal axis variable of the spectrum. Indicates a brand index; Indicates the number of brands.

[0070] In step S202, the reference spectral data of each brand of asphalt in the first database are used as a reference standard, and the vector difference between the spectral data of each brand of asphalt under different aging time parameters and the corresponding reference spectral data is calculated by numerical calculation method.

[0071] Specifically, for each brand of asphalt, the intensity values ​​of each wavenumber point of its spectral data under various aging time parameters are subtracted point by point from the corresponding wavenumber point intensity values ​​of the brand's reference spectral data to obtain a vector difference reflecting the spectral change of the brand's asphalt under the corresponding aging conditions relative to the reference state.

[0072] Similarly, the vector differences of this brand of asphalt were calculated for all aging time parameters, and the same calculation process was performed for other brands of asphalt. The calculation of vector differences eliminates the inherent spectral differences between different brands of asphalt under baseline conditions, highlighting the changes in spectral characteristics caused purely by aging.

[0073] For example, the reference spectral data for each brand of asphalt can be expressed as follows:

[0074] ;

[0075] In the formula, Indicate brand The baseline spectral data; Indicate brand The The reference spectrum is a set of reference spectra; m represents the number of reference spectra.

[0076] The vector difference between the spectral data of various brands of asphalt under different aging time parameters and the corresponding reference spectral data can be expressed as:

[0077] .

[0078] In step S203, the aging offset direction of the corresponding brand of asphalt is determined by statistical analysis of the vector difference of the same brand of asphalt under different aging time parameters, and mathematical methods such as principal component analysis or averaging are used.

[0079] Specifically, the vector differences calculated for each brand of asphalt under various aging time parameters are used to form a data matrix. Principal component analysis is used to extract the principal component direction of this matrix, or statistical averaging is used to calculate the average change direction, which serves as the aging shift direction for that brand of asphalt. This aging shift direction characterizes the dominant trend of the brand's asphalt evolution from the baseline spectral state to the aged spectral state in the multidimensional spectral feature space, reflecting the unique aging mechanism and molecular structure change pattern of that brand's asphalt.

[0080] By determining the unique aging offset direction for each brand of asphalt, different brands of asphalt can be effectively distinguished by their respective change trends even when they are at the same level of aging.

[0081] For example, the aging offset direction can be represented as: .

[0082] In step S204, the vector difference of each brand of asphalt under different aging time parameters is projected onto the corresponding aging offset direction to obtain the aging degree projection value, and the aging evolution function corresponding to each brand of asphalt is obtained by function fitting method.

[0083] Specifically, for each brand of asphalt, the vector difference of its value under each aging time parameter is multiplied by the aging offset direction of that brand to obtain a series of projected values ​​reflecting the degree of aging. Subsequently, a data point set is established with the aging time parameter as the independent variable and the projected values ​​as the dependent variable. The data point set is then nonlinearly fitted using mathematical models such as exponential functions, logarithmic functions, or polynomial functions to obtain an aging evolution function describing the aging evolution law of that brand of asphalt.

[0084] The aging evolution function establishes a continuous mapping relationship between the aging time parameter and the projected value of the aging degree, realizing the mathematical modeling of the aging process of this brand of asphalt, so that the target model can predict the aging degree of this brand of asphalt at any aging time point.

[0085] For example, the calculation process of the projection value can be represented as follows:

[0086]

[0087] In the formula, Indicate brand Aging time The projection value below, Representing vectors The square of the second norm is the energy magnitude of the aging direction vector.

[0088] The aging evolution function (exponential form) can be expressed as:

[0089] ;

[0090] In the formula, The fitting parameters represent the aging evolution function.

[0091] In step S205, the reference spectral data, aging offset direction and aging evolution function of each brand of asphalt are integrated through a linear combination mathematical form to construct a complete aging trajectory function.

[0092] Specifically, for each brand of asphalt, its baseline spectral data is used as the starting reference point. The vector product of the output value of the aging evolution function and the aging offset direction is used as the aging change. The aging trajectory function of that brand of asphalt under any aging time parameter is obtained by linearly adding the two. The aging trajectory function realizes a continuous trajectory description from the baseline spectral state to any degree of aging, enabling the second database to contain complete aging evolution information of each brand of asphalt.

[0093] For example, the aging trajectory function can be expressed as:

[0094] .

[0095] By adopting the technical solution of this application, spectral data of asphalt from multiple brands under different aging time parameters are obtained, providing a data foundation for constructing the aging trajectory function. Vector differences calculated based on benchmark spectral data eliminate the inherent spectral differences between different brands of asphalt, highlighting the changes in spectral characteristics caused by aging. Determining the aging offset direction based on the vector difference allows different brands of asphalt to be accurately distinguished by their unique changing trends even at the same aging degree, solving the problem of misjudgment caused by the blurring of brand characteristics due to aging. By fitting the projection value of the aging degree, an aging evolution function is obtained, realizing mathematical modeling from discrete aging time points to a continuous aging process, overcoming the limitation of only being able to identify asphalt at specific aging stages. Constructing the aging trajectory function expands each brand of asphalt from static spectral feature points to a dynamic trajectory describing the complete aging process.

[0096] The process of analyzing spectral information using a target model will be explained in detail below.

[0097] Specifically, it may include the following steps:

[0098] Step S301: Construct a decomposition model of spectral information based on the target model; the decomposition model is the sum of the linear combination of the benchmark spectral data of each brand and the aging trajectory function and the residual terms; the asphalt of each brand in the decomposition model includes virgin material components and recycled material components, the virgin material component is the product of the virgin material proportion coefficient and the benchmark spectral data of the corresponding brand, and the recycled material component is the product of the recycled material proportion coefficient and the value of the aging trajectory function of the corresponding brand.

[0099] Step S302: Solve the decomposition model to obtain the virgin material ratio coefficient, recycled material ratio coefficient and corresponding aging time parameters of each brand of asphalt in the mixed asphalt.

[0100] Step S303: Determine the brand information and proportion information of the second asphalt based on the new material proportion coefficient;

[0101] Step S304: Determine the brand information, proportion information, and aging degree information of the first asphalt based on the recycled material ratio coefficient and aging time parameters.

[0102] In step S301, a decomposition model of spectral information is constructed based on the target model. The core of the decomposition model is to regard the spectral information of the mixed asphalt as a linear superposition of the spectral contributions of each brand of asphalt component under different aging states. The construction process first represents the spectral information of the mixed asphalt in a mathematical decomposition form, and determines the quantitative analysis of the mixed asphalt components by decomposing the complex mixed spectral signal into multiple independent components.

[0103] Specifically, this is achieved by further subdividing each brand of asphalt component in the blended asphalt into two parts: virgin asphalt and recycled asphalt.

[0104] The mathematical expression of the new material composition is the product of the new material ratio coefficient and the benchmark spectral data of the corresponding brand. The new material ratio coefficient quantifies the relative content of the brand's asphalt in the mixed asphalt in an unaged state. The benchmark spectral data comes from the standard spectral characteristics of the brand's asphalt in the new material state in the first database, thus ensuring that the new material composition can accurately reflect the contribution of the brand's unaged asphalt to the spectral information of the mixed asphalt.

[0105] The mathematical expression of the recycled material component is the product of the recycled material proportion coefficient and the value of the aging trajectory function of the corresponding brand. The recycled material proportion coefficient quantifies the relative content of the brand of asphalt in the mixed asphalt in an aged state. The value of the aging trajectory function comes from the spectral characteristics of the brand of asphalt under specific aging time parameters in the second database. Through calculation, the recycled material component can simultaneously reflect the influence of the presence ratio and specific aging degree of the brand of asphalt on the spectral information of the mixed asphalt.

[0106] The decomposition model may also include residual terms, which are set to capture spectral variations that the decomposition model cannot fully explain. These variations may originate from unknown brand components not included in the database, spectral drift caused by changes in environmental conditions, instrument noise interference, or approximate errors in the model's linear assumptions.

[0107] For example, the decomposition model can be represented as:

[0108] ;

[0109] In the formula, Represents the spectral information of mixed asphalt; This represents the new material ratio coefficient for brand i. This represents the proportion of recycled materials in brand i. The aging trajectory function represents brand i, and the aging time parameter represents the aging trajectory function. Spectral characteristics below; Represents the residual term;

[0110] The composition of the new material can be expressed as follows:

[0111] ;

[0112] In the formula, This indicates the new material composition of brand i;

[0113] The components of recycled materials can be represented as follows:

[0114] ;

[0115] In the formula, This indicates the recycled material composition of brand i.

[0116] In step S302, the virgin asphalt ratio coefficient, recycled asphalt ratio coefficient, and aging time parameters for each brand of asphalt are first initialized to ensure that the virgin asphalt ratio coefficient and recycled asphalt ratio coefficient are non-negative and that the aging time parameter is within a reasonable time range. Constraints are used to ensure the physical meaning of the parameters and the stability of the mathematical solution. Subsequently, an optimization function can be constructed to quantify the fitting quality of the decomposition model. The optimization function adopts the least squares criterion, that is, minimizing the sum of squared Euclidean distances between the actual spectral information and the model's predicted spectral information.

[0117] In one feasible implementation, the solution process can employ an alternating optimization strategy to handle the nonlinear coupling between parameters, decomposing the complex multivariate optimization problem into several relatively simple subproblems for solution. The algorithm continuously updates the parameter estimates through multiple iterations, sequentially optimizing the scaling factor and aging time parameters in each iteration until the change in the objective function value is less than a preset convergence threshold or the maximum number of iterations is reached.

[0118] For example, the objective of solving the parameters can be expressed as:

[0119] ;

[0120] In step S303, the brand information determination process involves iterating through all the virgin material ratio coefficients corresponding to each brand, using a numerical comparison algorithm to find the maximum value and its corresponding brand index, and then using the complete information of that brand, including manufacturer identification, product model, and technical specifications, as the brand information for the second asphalt. This method ensures that the brand information of the second asphalt accurately reflects the main source of the asphalt components with lower aging levels in the mixed asphalt. When multiple brands have similar virgin material ratio coefficient values, the determination process prioritizes the brand with the largest value as the primary brand, while recording information from other significant brands as supplementary references.

[0121] Similarly, the process of determining the proportion information involves using the virgin material proportion coefficient of the main brand as the base proportion of the second asphalt in the mixed asphalt, while considering the contribution of the virgin material proportion coefficients of other brands to calculate the overall proportion of the second asphalt. When only one brand in the mixed asphalt has a significant virgin material proportion coefficient, that brand's virgin material proportion coefficient is directly used as the proportion information of the second asphalt. When multiple brands have a certain virgin material proportion coefficient, the overall proportion of the second asphalt is obtained by weighting the virgin material proportion coefficients of all brands. The weighting method can be adjusted according to the relative magnitude of the virgin material proportion coefficients of each brand.

[0122] Step S304: The determination of brand information is achieved by numerically filtering the recycled material ratio coefficient of each brand. A predetermined threshold can be set to filter the recycled material ratio coefficient. Brands with values ​​greater than the threshold are identified as valid constituent brands of the first asphalt, and the corresponding manufacturer identification, product model, and other complete brand information are extracted.

[0123] When multiple brands are found to have a significant recycled material ratio coefficient, the importance of the brands is ranked by numerical comparison. The brand with the largest recycled material ratio coefficient is designated as the primary brand of asphalt, and the remaining brands are designated as secondary brands in order of numerical value.

[0124] The proportion information is determined by numerical aggregation, summing the recycled material proportion coefficients corresponding to all valid brands to obtain the overall content proportion of the first asphalt in the mixed asphalt. Simultaneously, by calculating the relative proportion of each brand's recycled material proportion coefficient in the sum, the distribution proportion of each brand component within the first asphalt is obtained, forming a hierarchical proportion information structure.

[0125] The determination of aging degree information is based on a comparative analysis of aging time parameters and preset grading standards. The aging time parameters are divided into different aging levels according to predetermined time intervals, including light aging, moderate aging, and heavy aging. The corresponding aging level is determined by judging the time interval to which the aging time parameters belong. When the first asphalt contains multiple brands, a weighted average or a method determined by the dominant brand is used to comprehensively assess the overall aging degree.

[0126] By adopting the technical solution of this application, the decomposition model constructed based on the target model can decompose the complex spectral information of mixed asphalt into a linear combination of virgin and recycled components of each brand, realizing the simultaneous processing of brand identification and aging degree analysis. The virgin proportion coefficient, recycled proportion coefficient, and aging time parameters obtained by solving the decomposition model provide complete quantitative information for the component analysis of mixed asphalt.

[0127] Based on the above embodiments, the process of solving the decomposition model is described below, which may include the following steps:

[0128] Step S401: Construct the optimization function; the optimization function includes a residual squared term and a sparse constraint term; the residual squared term is used to minimize the fitting error between the spectral information and the decomposition model, and the sparse constraint term is used to limit the number of brands participating in the decomposition.

[0129] Step S402: Based on the least squares method, the aging time parameter and the proportion coefficient in the optimization function are alternately fixed to iteratively solve the optimization function; with the aging time parameter fixed, the proportion coefficient of new material and the proportion coefficient of recycled material of each brand of asphalt are solved; with the proportion coefficient fixed, the aging time parameter of each brand of asphalt is solved.

[0130] In step S401, the optimization function can be constructed based on the least squares criterion to establish the residual squared term. The magnitude of the fitting error is quantified by calculating the sum of squared Euclidean distances between the actual spectral information of the mixed asphalt and the predicted spectral information of the decomposition model.

[0131] Specifically, the mathematical expression of the residual squared term squares the difference between the spectral information of the mixed asphalt and the linear combination results of the components of each brand of virgin and recycled materials, and then sums them up. This allows the optimization process to minimize the overall deviation between the model's predictions and the actual measurements. The design of the residual squared term ensures that the decomposition model can fit the spectral characteristics of the mixed asphalt as accurately as possible.

[0132] The sparsity constraint term determines the effective limit on the number of brands participating in the decomposition by applying L1 regularization constraints to the virgin material ratio coefficient and recycled material ratio coefficient of each brand of asphalt.

[0133] Specifically, the sparse constraint term calculates a weighted sum of the absolute values ​​of all proportionality coefficients, and the constraint strength is controlled by adjusting the regularization coefficient, so that the optimization process tends to select a few major brands for spectral decomposition rather than distributing the contribution across a large number of brands.

[0134] The design of the sparse constraint term avoids the instability and unclear physical meaning of the solution caused by too many brands through mathematical constraints. The optimization function balances the fitting accuracy and the sparsity of the solution by linearly combining the residual squared term with the sparse constraint term. The choice of regularization coefficient determines the trade-off between the two. A larger regularization coefficient will produce a sparser solution but may reduce the fitting accuracy, while a smaller regularization coefficient will improve the fitting accuracy but may produce too many non-zero coefficients.

[0135] For example, the optimization function can be defined as:

[0136] ;

[0137] In the formula, Represents the optimization function; This represents the sparse constraint coefficient, used to constrain the number of non-zero proportional coefficients.

[0138] In step S402, the solution process adopts an alternating optimization strategy to handle the nonlinear coupling relationship between the virgin material ratio coefficient, the recycled material ratio coefficient, and the aging time parameter in the optimization function. This is achieved by decomposing the complex multivariate optimization problem into several relatively simple sub-problems for solution.

[0139] The initialization process of iterative solution first sets reasonable initial values ​​for the proportion coefficients of new material and recycled material of each brand of asphalt. Random initialization or heuristic initialization based on prior knowledge is usually adopted. At the same time, the aging time parameter is initialized to an intermediate value within a preset range to ensure the stability of algorithm convergence.

[0140] With a fixed aging time parameter, the aging time parameter can be treated as a known constant. In this case, the decomposition model is transformed into a linear regression problem concerning the proportion coefficients of virgin materials and recycled materials. By constructing a feature matrix from the benchmark spectral data of each brand and the aging trajectory function values ​​under the corresponding aging time parameters, the optimal values ​​of the proportion coefficients of virgin materials and recycled materials are solved using the least squares method with L1 regularization.

[0141] Furthermore, the subproblems can be solved using mature convex optimization algorithms such as coordinate descent or proximal gradient, by iteratively updating each scaling factor until convergence to obtain the optimal scaling allocation under the current aging time parameters.

[0142] With fixed proportion coefficients, the virgin asphalt proportion coefficient and the recycled asphalt proportion coefficient can be considered as known constants. In this case, only the aging time parameter of each brand of asphalt needs to be optimized. For each brand with a non-zero recycled asphalt proportion coefficient, the optimal aging time parameter is determined by minimizing the fitting error between the recycled asphalt component of that brand and the corresponding part in the actual spectral information.

[0143] Since the aging trajectory function is usually a nonlinear function of the aging time parameter, the subproblem is solved using a one-dimensional nonlinear optimization method, including grid search to find the optimal value by discrete sampling within a preset time range, or continuous optimization using optimization algorithms based on derivative information such as gradient descent and Newton's method.

[0144] The iterative optimization process involves repeatedly executing two sub-steps: solving for the scaling factor with a fixed aging time parameter and solving for the aging time parameter with a fixed scaling factor. In each iteration, the estimated values ​​of all parameters are updated sequentially until the algorithm converges. Convergence is determined by monitoring the magnitude of change in the optimization function value. The algorithm terminates when the relative change in the optimization function value between two consecutive iterations is less than a preset convergence threshold, or when a preset maximum number of iterations is reached to avoid infinite loops. Appropriate learning rate control strategies are used during parameter updates in the iteration process to balance convergence speed and stability. An excessively large learning rate may cause the algorithm to oscillate or diverge, while an excessively small learning rate will make the convergence process too slow.

[0145] For example, the process of solving for the scaling factor can be expressed as:

[0146] ;

[0147] The process of solving for the aging time parameter can be expressed as:

[0148] ;

[0149] The iterations in the above solution process can be expressed as:

[0150] ;

[0151] ;

[0152] In the formula, k represents the iteration number index; , , Let represent the parameter estimates for the k-th iteration.

[0153] By adopting the technical solution of this application, the constructed optimization function ensures the accurate fitting of the decomposition model to the spectral information of mixed asphalt through the residual squared term, and effectively controls the number of brands participating in the decomposition through the sparse constraint term, avoiding the problems of instability and unclear physical meaning. The alternating optimization strategy based on the least squares method transforms the complex nonlinear multivariate optimization problem into several relatively simple subproblems, improving the solution efficiency and algorithm stability.

[0154] Based on the above embodiments, as an optional embodiment, the step of determining the test results of mixed asphalt may further include the following steps:

[0155] Step S501: Determine the brand corresponding to the largest new material proportion coefficient as the main brand of mixed asphalt;

[0156] Step S502: Calculate the total recycled material ratio of the mixed asphalt; the total recycled material ratio is the sum of the recycled material ratio coefficients of each brand of asphalt.

[0157] Step S503: Determine the aging level of the first asphalt based on the aging time parameter; the aging level is determined by comparing the aging time parameter with a preset time threshold range, including light aging, moderate aging and heavy aging.

[0158] Step S504: If the value of the residual term is greater than or equal to the preset residual threshold, it is determined that there is an unknown brand component or an abnormality in the mixed asphalt.

[0159] In step S501, the new material ratio coefficient values ​​of each brand can be read sequentially, the coefficient with the largest value can be determined by comparison and calculation, and the brand identification information corresponding to the coefficient can be extracted, including complete brand attributes such as manufacturer name, product model and technical specifications, and it can be determined as the main brand of mixed asphalt.

[0160] When multiple brands have similar new material ratio coefficients, the brand with the largest value is selected as the leading brand, while other significant brand information is recorded as supplementary references.

[0161] In step S502, the recycling ratio coefficients of each brand can be iterated, and an additive calculation is used to obtain a numerical result reflecting the total recycled content in the mixed asphalt. The calculation process considers the superposition effect of recycled components from different brands to ensure that the total recycled ratio can accurately reflect the overall recycled content level of the mixed asphalt.

[0162] When the recycling ratio coefficient of certain brands is zero, those brands are not included in the cumulative calculation; only brands with a non-zero recycling ratio coefficient are summed.

[0163] In step S503, time threshold ranges corresponding to light aging, moderate aging and heavy aging can be preset, and the corresponding aging level can be determined by judging the range range to which the aging time parameter of the first asphalt belongs.

[0164] Among them, mild aging corresponds to a smaller aging time parameter value, indicating that the asphalt still maintains good performance; moderate aging corresponds to a moderate degree of aging time parameter value, indicating that the performance of the asphalt has significantly deteriorated but can still be used with appropriate treatment; and severe aging corresponds to a larger aging time parameter value, indicating that the performance of the asphalt has seriously deteriorated and needs to be used with caution or undergo deep treatment.

[0165] When the first asphalt contains multiple brands, a weighted average method is used to allocate the corresponding aging time parameters according to the recycled material ratio coefficient of each brand, and the comprehensive aging time parameters are calculated before the grade is determined.

[0166] In step S504, the Euclidean distance between the actual spectral information of the mixed asphalt and the fitting result of the decomposition model can be calculated to obtain a quantified residual value, and this value is compared with a preset residual threshold determined based on historical test data and instrument accuracy.

[0167] When the residual value is greater than or equal to a preset threshold, it indicates possible anomalies such as unknown brand components not included in the database, spectral drift caused by abnormal environmental conditions, measurement errors caused by instrument malfunction, or contamination during sample preparation. The anomaly detection mechanism automatically identifies potential detection problems and alerts operators to perform further sample verification or instrument calibration.

[0168] By adopting the technical solution of this application, the main brand corresponding to the maximum proportion coefficient of new material can be determined to clarify the main brand composition of the mixed asphalt. The calculation of the total proportion of recycled material realizes the accurate quantification of the amount of recycled material added. The aging level is determined according to the aging time parameter, which provides a grading reference for asphalt performance evaluation. The anomaly detection mechanism based on the residual term ensures the reliability of the test results.

[0169] Based on the above embodiments, as an optional embodiment, after obtaining the spectral information of the mixed asphalt, the spectral information can be preprocessed, specifically including the following steps:

[0170] Step S601: Normalize the spectral intensity of each wavenumber point in the spectral information based on a preset reference frequency band.

[0171] Step S602: Denoise reduction processing is performed on the normalized spectral information.

[0172] In step S601, the normalization process eliminates the influence of systematic bias and intensity variation under different detection conditions by selecting a preset reference frequency band. Therefore, the wavenumber range of the preset reference frequency band can be determined first. This band is typically selected from the characteristic peak regions of asphalt materials that are relatively stable and not easily affected by aging. By traversing all wavenumber points within this reference frequency band, the maximum value of the spectral intensity is found through numerical comparison calculations as the normalization benchmark. Subsequently, the spectral intensity of each wavenumber point in the spectral information is divided by this maximum value to obtain the normalized spectral data.

[0173] Normalization ensures that spectral data from different detection times, environmental conditions, or instruments have a uniform intensity standard, eliminating spectral intensity fluctuations caused by external factors such as temperature changes, humidity differences, and instrument response changes.

[0174] In step S602, the noise reduction process can employ wavelet transform to remove noise from the normalized spectral information. Appropriate wavelet basis functions can be selected to perform multi-scale wavelet decomposition on the normalized spectral data, decomposing the spectral signal into approximation coefficients and detail coefficients at different frequency levels. By analyzing the amplitude distribution characteristics of the detail coefficients at each level, a thresholding method is used to suppress or eliminate high-frequency noise coefficients. Subsequently, the denoised coefficients can be used to reconstruct the spectral signal using inverse wavelet transform, obtaining smooth spectral data that retains its main characteristics.

[0175] By adopting the technical solution of this application, the normalization processing based on the preset reference frequency band eliminates the influence of changes in detection conditions on spectral intensity, ensuring the consistency and comparability of spectral data from different sources. Wavelet transform denoising processing removes various noise interferences while retaining effective spectral feature information.

[0176] Figure 2 This schematic diagram illustrates a structural block diagram of an asphalt testing device provided in this application, which may include:

[0177] The acquisition module is used to acquire the spectral information of the mixed asphalt, which includes at least a first asphalt and a second asphalt; the aging degree of the first asphalt is greater than that of the second asphalt.

[0178] The analysis module is used to analyze spectral information through the target model to obtain brand information, ratio information, and aging degree information of the first and second asphalt.

[0179] The determination module is used to determine the test results of the mixed asphalt based on the brand information, proportion information, and aging degree information of the first and second asphalt.

[0180] The target model is constructed based on a first database and a second database. The first database includes benchmark spectral data of asphalt from multiple brands, and the second database includes aging trajectory functions of asphalt from multiple brands. The aging trajectory functions characterize the evolution of the aging degree of asphalt from multiple brands under different aging time parameters.

[0181] Based on the above embodiments, as an optional embodiment, the asphalt testing device further includes a construction module for acquiring spectral data of asphalt from multiple brands under different aging time parameters; calculating the vector difference between the spectral data of each brand of asphalt under different aging time parameters and the corresponding reference spectral data based on the reference spectral data of each brand of asphalt; determining the aging offset direction of the corresponding brand of asphalt based on the vector difference; fitting the aging degree projection value of each brand of asphalt under different aging time parameters to obtain the aging evolution function corresponding to each brand of asphalt; and constructing the mapping relationship between the reference spectral data of each brand of asphalt and the corresponding aging offset direction and aging evolution function to obtain the aging trajectory function of each brand of asphalt.

[0182] Based on the above embodiments, as an optional embodiment, the analysis module is also used to construct a decomposition model of spectral information based on the target model; the decomposition model is the sum of the linear combination and residual terms of the reference spectral data of each brand and the aging trajectory function; the asphalt of each brand in the decomposition model includes virgin material components and recycled material components, the virgin material component is the product of the virgin material proportion coefficient and the reference spectral data of the corresponding brand, and the recycled material component is the product of the recycled material proportion coefficient and the value of the aging trajectory function of the corresponding brand; solving the decomposition model yields the virgin material proportion coefficient, the recycled material proportion coefficient, and the corresponding aging time parameters of each brand of asphalt in the mixed asphalt; the brand information and proportion information of the second asphalt are determined based on the virgin material proportion coefficient; the brand information, proportion information, and aging degree information of the first asphalt are determined based on the recycled material proportion coefficient and the aging time parameters.

[0183] Based on the above embodiments, as an optional embodiment, the analysis module is also used to construct an optimization function; the optimization function includes a residual square term and a sparsity constraint term; the residual square term is used to minimize the fitting error between the spectral information and the decomposition model, and the sparsity constraint term is used to limit the number of brands participating in the decomposition; the aging time parameter and the proportion coefficient in the optimization function are alternately fixed based on the least squares method to iteratively solve the optimization function; with the aging time parameter fixed, the virgin material proportion coefficient and recycled material proportion coefficient of each brand of asphalt are solved; with the proportion coefficient fixed, the aging time parameter of each brand of asphalt is solved.

[0184] Based on the above embodiments, as an optional embodiment, the determining module is further used to determine the brand corresponding to the largest virgin material ratio coefficient as the main brand of the mixed asphalt; calculate the total recycled material ratio of the mixed asphalt; the total recycled material ratio is the sum of the recycled material ratio coefficients of each brand of asphalt; determine the aging level of the first asphalt according to the aging time parameter; the aging level is determined by comparing the aging time parameter with a preset time threshold range, including light aging, moderate aging and heavy aging; if the value of the residual term is greater than or equal to the preset residual threshold, it is determined that there is an unknown brand component or an abnormality in the mixed asphalt.

[0185] Based on the above embodiments, as an optional embodiment, the acquisition module is further used to normalize the spectral intensity of each wavenumber point in the spectral information based on a preset reference frequency band; and to perform noise reduction processing on the normalized spectral information.

[0186] Figure 3 The diagram illustrates a structural block diagram of an electronic device suitable for implementing the methods described above, according to an embodiment of this application. Figure 3 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0187] like Figure 3 As shown, an electronic device according to an embodiment of this application includes a processor 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage portion 308 into a random access memory (RAM) 303. The processor 301 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 301 may also include onboard memory for caching purposes. The processor 301 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of this application.

[0188] RAM 303 stores various programs and data required for the operation of the electronic device. Processor 301, ROM 302, and RAM 303 are interconnected via bus 304. Processor 301 executes various operations of the method flow according to embodiments of this application by executing programs in ROM 302 and / or RAM 303. It should be noted that the programs may also be stored in one or more memories other than ROM 302 and RAM 303. Processor 301 may also execute various operations of the method flow according to embodiments of this application by executing programs stored in said one or more memories.

[0189] According to embodiments of this application, the electronic device may further include an input / output (I / O) interface 305, which is also connected to a bus 304. The electronic device may also include one or more of the following components connected to the input / output (I / O) interface 305: an input section 306 including a keyboard, mouse, etc.; an output section 307 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN card, modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the input / output (I / O) interface 305 as needed. A removable medium 311, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 310 as needed so that computer programs read from it can be installed into the storage section 308 as needed.

[0190] According to embodiments of this application, the method flow according to embodiments of this application can be implemented as a computer software program. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable storage medium, the computer program containing program code for performing the methods shown in the flowchart. In such embodiments, the computer program can be downloaded and installed from a network via communication section 309, and / or installed from removable medium 311. When the computer program is executed by processor 301, it performs the functions defined in the system of embodiments of this application. According to embodiments of this application, the systems, devices, modules, units, etc., described above can be determined by computer program modules.

[0191] This application also provides a computer-readable storage medium, which may be included in the device / system / system described in the above embodiments; or it may exist independently and not assembled into the device / system / system. The aforementioned computer-readable storage medium carries one or more programs, which, when executed, implement the method according to the embodiments of this application.

[0192] According to embodiments of this application, the computer-readable storage medium can be a non-volatile computer-readable storage medium. Examples include, but are not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this application, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device.

[0193] For example, according to embodiments of this application, a computer-readable storage medium may include the ROM 302 and / or RAM 303 described above and / or one or more memories other than ROM 302 and RAM 303.

[0194] Embodiments of this application also include a computer program product comprising a computer program containing program code for performing the methods provided in the embodiments of this application. When the computer program product is run on an electronic device, the program code is used to enable the electronic device to implement the methods provided in the embodiments of this application.

[0195] When the computer program is executed by the processor 301, it performs the functions defined in the system / system of the embodiments of this application. According to the embodiments of this application, the systems, modules, units, etc., described above can be determined by computer program modules.

[0196] In one embodiment, the computer program may rely on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may also be transmitted and distributed in the form of signals over a network medium, and may be downloaded and installed via communication section 309, and / or installed from removable medium 311. The program code contained in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof.

[0197] According to embodiments of this application, program code for executing the computer programs provided in the embodiments of this application can be written in any combination of one or more programming languages. Specifically, these computational programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C", or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0198] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, can be determined by a dedicated hardware-based system performing the specified function or operation, or by a combination of dedicated hardware and computer instructions. Those skilled in the art will understand that the features recited in the various embodiments and / or claims of this application can be combined and / or combined in various ways, even if such combinations or combinations are not expressly stated in this application. In particular, the various embodiments and / or features described in the claims of this application may be combined and / or combined in various ways without departing from the spirit and teachings of this application. All such combinations and / or combinations fall within the scope of this application.

[0199] The embodiments of this application have been described above. However, these embodiments are merely illustrative and not intended to limit the scope of this application. Although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be used advantageously in combination. The scope of this application is defined by the appended claims and their equivalents. Various substitutions and modifications can be made by those skilled in the art without departing from the scope of this application, and all such substitutions and modifications should fall within the scope of this application.

Claims

1. A method for testing asphalt, characterized in that, include: Obtain spectral information of the mixed asphalt, wherein the mixed asphalt includes at least a first asphalt and a second asphalt; The aging degree of the first asphalt is greater than that of the second asphalt; The spectral information is analyzed using a target model to obtain brand information, proportion information, and aging degree information for the first and second asphalts. Specifically, this includes: constructing a decomposition model of the spectral information based on the target model; the decomposition model is the sum of a linear combination of the baseline spectral data of each brand and the aging trajectory function, plus residual terms; in the decomposition model, each brand of asphalt includes virgin and recycled components, the virgin component being the product of the virgin proportion coefficient and the corresponding brand's baseline spectral data, and the recycled component being the product of the recycled proportion coefficient and the value of the corresponding brand's aging trajectory function; solving the decomposition model to obtain the virgin proportion coefficient, recycled proportion coefficient, and corresponding aging time parameters for each brand of asphalt in the mixed asphalt; determining the brand and proportion information of the second asphalt based on the virgin proportion coefficient; and determining the brand, proportion, and aging degree information of the first asphalt based on the recycled proportion coefficient and the aging time parameters. Solving the decomposition model to obtain the virgin asphalt ratio coefficient, recycled asphalt ratio coefficient, and corresponding aging time parameters for each brand of asphalt in the mixed asphalt includes: constructing an optimization function; the optimization function includes a residual square term and a sparsity constraint term; the residual square term is used to minimize the fitting error between the spectral information and the decomposition model, and the sparsity constraint term is used to limit the number of brands participating in the decomposition; the aging time parameter and the ratio coefficient in the optimization function are alternately fixed based on the least squares method to iteratively solve the optimization function; with the aging time parameter fixed, the virgin asphalt ratio coefficient and recycled asphalt ratio coefficient for each brand of asphalt are solved; with the ratio coefficient fixed, the aging time parameter for each brand of asphalt is solved. Based on the brand information, proportion information, and aging degree information of the first asphalt and the second asphalt, the test results of the mixed asphalt are determined. The target model is constructed based on a first database and a second database; the first database includes reference spectral data of asphalt from multiple brands, and the second database includes aging trajectory functions of asphalt from multiple brands; the aging trajectory functions characterize the evolution of the aging degree of asphalt from multiple brands under different aging time parameters.

2. The asphalt testing method according to claim 1, characterized in that, The aging trajectory function is constructed based on the baseline spectral data of asphalt of each brand in the first database through an aging evolution function; the aging evolution function characterizes the evolution law of the spectral characteristics of asphalt over time. The aging trajectory function of the asphalt of each brand has an aging offset direction; the aging offset direction characterizes the trend of change of the reference spectral data of the asphalt from the reference spectral state to the aging spectral state.

3. The asphalt testing method according to claim 2, characterized in that, Also includes: Obtain spectral data of asphalt from multiple brands under different aging time parameters; Based on the reference spectral data of asphalt from each brand, the vector difference between the spectral data of each brand of asphalt under different aging time parameters and the corresponding reference spectral data is calculated. The aging offset direction of the corresponding brand of asphalt is determined based on the vector difference; By fitting the aging degree projection values ​​of each brand of asphalt under different aging time parameters, the aging evolution function corresponding to each brand of asphalt is obtained. The mapping relationship between the baseline spectral data of each brand of asphalt and the corresponding aging offset direction and aging evolution function is constructed to obtain the aging trajectory function of each brand of asphalt.

4. The asphalt testing method according to claim 1, characterized in that, The determination of the test results for the mixed asphalt based on the brand information, proportion information, and aging degree information of the first and second asphalts includes: The brand corresponding to the largest proportion of new material is determined as the main brand of the mixed asphalt. Calculate the total recycled material ratio of the mixed asphalt; the total recycled material ratio is the sum of the recycled material ratio coefficients of each brand of asphalt; The aging level of the first asphalt is determined based on the aging time parameter; the aging level is determined by comparing the aging time parameter with a preset time threshold range, including mild aging, moderate aging and severe aging. If the value of the residual term is greater than or equal to a preset residual threshold, it is determined that there is an unknown brand component or an abnormality in the mixed asphalt.

5. The asphalt testing method according to claim 1, characterized in that, After obtaining the spectral information of the mixed asphalt, the process further includes: The spectral intensity of each wavenumber point in the spectral information is normalized based on a preset reference frequency band. The spectral information after normalization is subjected to noise reduction processing.

6. An asphalt testing apparatus for implementing the asphalt testing method according to any one of claims 1 to 5, characterized in that, include: An acquisition module is used to acquire spectral information of the mixed asphalt, wherein the mixed asphalt includes at least a first asphalt and a second asphalt; The aging degree of the first asphalt is greater than that of the second asphalt; The analysis module is used to analyze the spectral information through the target model to obtain the brand information, proportion information and aging degree information of the first asphalt and the second asphalt; The determination module is used to determine the test results of the mixed asphalt based on the brand information, proportion information, and aging degree information of the first asphalt and the second asphalt. The target model is constructed based on a first database and a second database; the first database includes reference spectral data of asphalt from multiple brands, and the second database includes aging trajectory functions of asphalt from multiple brands; the aging trajectory functions characterize the evolution of the aging degree of asphalt from multiple brands under different aging time parameters.

7. An electronic device, comprising: One or more processors; Memory, used to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the asphalt detection method according to any one of claims 1 to 5.

8. A computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, cause the processor to implement the asphalt testing method according to any one of claims 1 to 5.

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