Method for establishing asphalt material database management system

Abstract

The invention relates to the technical field of asphalt material data processing, in particular to a method for establishing an asphalt material database management system. The method comprises the following steps: respectively carrying out imaging scanning on asphalt material samples with different gradation, different modifier types and different aging states by utilizing a scanning electron microscope, an atomic force microscope and X-ray tomography equipment to obtain multi-scale asphalt material microscopic image data; analyzing material sample microscopic characteristics according to the preprocessed multi-scale asphalt material microscopic image data to obtain asphalt material microscopic characteristic parameters; and creating an asphalt material database management system for the asphalt material microscopic characteristic parameters based on a relational database management system. According to the method, a multi-dimensional query asphalt material database is created through efficient association of the microstructure and the macroscopic performance of the asphalt material, and efficient storage and rapid query of asphalt material data are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of asphalt material data processing, and in particular to a method for establishing an asphalt material database management system. Background Art

[0002] Asphalt material is a complex composite material, and its performance is affected by many factors, including asphalt type, aggregate type, additives, grading design, microstructure, etc. In the field of asphalt materials, some database management systems have also emerged to store and manage the performance data of asphalt materials. These systems usually adopt a relational database model, store various parameters of asphalt materials as attributes in the database, and realize data retrieval and analysis through query statements. However, when dealing with complex materials such as asphalt materials, these traditional database management systems usually manage the material ID number as an independent field. This processing method easily ignores highly correlated subtle feature information and the complex relationship between the performance indicators of asphalt materials and the microstructural characteristics. As a result, when it is necessary to screen the material performance, the microstructural factors that play a key role in the performance cannot be effectively captured, resulting in low query efficiency. Especially when performing complex conditional queries, the query time will increase significantly when screening asphalt materials that meet multiple performance indicators at the same time, which is difficult to meet the needs of practical applications. Summary of the invention

[0003] Based on this, the present invention proposes a method for establishing an asphalt material database management system to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for establishing an asphalt material database management system comprises the following steps: Using a scanning electron microscope, an atomic force microscope and an X-ray tomography device, imaging and scanning are performed on asphalt material samples of different gradations, different modifier types and different aging states, respectively, to obtain multi-scale asphalt material microscopic image data; wherein the multi-scale asphalt material microscopic image data includes an asphalt material surface morphology image, an asphalt material surface roughness image and an asphalt material three-dimensional structure image; Performing denoising, contrast enhancement and geometric distortion correction preprocessing operations on the multi-scale asphalt material microscopic image data; Analyzing the microscopic characteristics of the material sample according to the pre-processed multi-scale asphalt material microscopic image data to obtain the microscopic characteristic parameters of the asphalt material; An asphalt material database management system is created for the microscopic characteristic parameters of the asphalt material based on a relational database management system; wherein the asphalt material database management system supports multi-dimensional queries based on preparation parameters, microscopic characteristic parameters and macroscopic performance data, and outputs asphalt material data that meets the conditions.

[0005] The present invention obtains multi-scale microstructure data of asphalt materials, including surface morphology, roughness and three-dimensional structure, etc., through advanced microscopic imaging technology, which provides a basis for in-depth understanding of the internal structure, component distribution and interaction of the material. These microstructure information is often the key factor in determining the macroscopic properties of asphalt materials. The microscopic characteristic parameters of asphalt materials are extracted from the pre-processed microscopic image data. These parameters cover the morphological characteristics of the material, such as the size, shape, distribution of particles, and the roughness and porosity of the surface. These microstructure parameters are closely related to the macroscopic properties of asphalt materials, such as mechanical properties, durability, and high temperature stability. These microscopic characteristic parameters are combined with the preparation parameters and macroscopic performance data of asphalt materials and integrated into an asphalt material database based on a relational database management system. Unlike the traditional method of managing material ID as an independent field only, the new system incorporates microscopic features into the database as key attributes, thereby establishing the relationship between the microstructure, preparation parameters and macroscopic properties of the material. This multi-dimensional design of the database enables the system to not only store and manage large amounts of data, but also to query complex conditions. For example, users can simultaneously set multiple conditions such as preparation parameters (such as asphalt type, aggregate gradation, additive type, etc.), microstructural characteristics (such as porosity, roughness) and macro performance indicators (such as compressive strength, elastic modulus) for screening, so as to quickly find asphalt materials that meet specific requirements. By incorporating microscopic feature data into the query conditions, asphalt materials with specific microstructures can be screened out more accurately. Therefore, a method for establishing an asphalt material database management system of the present invention obtains multi-scale microscopic image data of asphalt materials through scanning electron microscopes, atomic force microscopes and X-ray tomography equipment, and obtains microscopic feature parameters after preprocessing and analysis. Based on a relational database management system, a multi-dimensional query asphalt material database is created by combining preparation parameters, microscopic feature parameters and macroscopic performance data.

[0006] Preferably, the imaging scan comprises the following steps: According to the preset gradation range, modifier type and aging conditions, asphalt material samples with different gradations, different modifier types and different aging conditions are prepared, and the asphalt material samples are subjected to pretreatment operations such as cutting and polishing to obtain sample surfaces that meet the test requirements; Fixing the pretreated asphalt material sample on a sample stage of a scanning electron microscope, an atomic force microscope and an X-ray tomography device, and determining at least 5 different imaging areas according to the sample size and the target magnification; Start a scanning electron microscope, an atomic force microscope and an X-ray tomography device, scan the imaging areas one by one, and save the scanning image data of each area, to obtain a surface morphology image of the asphalt material, a surface roughness image of the asphalt material and a three-dimensional structure image of the asphalt material respectively; wherein, the surface morphology image of the asphalt material is obtained by a scanning electron microscope; the surface roughness image of the asphalt material is obtained by an atomic force microscope; and the three-dimensional structure image of the asphalt material is obtained by an X-ray tomography device.

[0007] The present invention ensures the representativeness and consistency of the samples by presetting the gradation range, modifier type and aging conditions, and performing sample preparation and pretreatment. This enables the acquired image data to truly reflect the microstructural characteristics of different types of asphalt materials. Furthermore, scanning electron microscopes, atomic force microscopes and X-ray tomography equipment are selected to observe microscopic features of different scales, respectively, to form a complementary relationship, and to comprehensively characterize the microstructural characteristics of asphalt materials. At least 5 different imaging areas are determined during the scanning process to ensure the coverage and representativeness of the scan, and to avoid the influence of the randomness of local samples on the results. The scanned image data of each area is scanned and saved one by one, providing a rich data basis for subsequent analysis and research, and ensuring the comprehensiveness and reliability of the data.

[0008] Preferably, the microscopic characteristic analysis of the material sample comprises the following steps: Identifying pore areas according to the three-dimensional structural image of the asphalt material, and performing pore characteristic analysis to obtain pore characteristic data; Based on the surface morphology image of the asphalt material and the three-dimensional structure image of the asphalt material, the aggregate particle contour is identified, and the aggregate contour feature analysis is performed to obtain aggregate feature data; The asphalt film is segmented according to the asphalt material surface morphology image, and the asphalt interface state is analyzed through the asphalt material surface roughness image to obtain the asphalt film characteristic data; The pore characteristic data, aggregate characteristic data and asphalt film characteristic data are quantified, and the relationship between the characteristic parameters is established to obtain the microscopic characteristic parameters of the asphalt material.

[0009] The present invention identifies the pore area from the three-dimensional structure image and performs characterization analysis, and key information such as porosity, pore size distribution, and pore connectivity can be obtained. These pore characteristic data are crucial for understanding the macroscopic properties of asphalt materials such as permeability and durability. Secondly, based on the surface morphology image and the three-dimensional structure image, the aggregate particle contour is identified and characteristic analysis is performed, and information such as the size, shape, distribution, and adhesion of the aggregate to asphalt can be obtained. By segmenting the asphalt film and analyzing the asphalt interface state in combination with the surface roughness image, information such as the thickness, uniformity, roughness, and interface bonding state of the asphalt film can be obtained. The extracted pore characteristic data, aggregate characteristic data, and asphalt film characteristic data are quantified, and the relationship between the characteristic parameters is established, and finally the microscopic characteristic parameters of the asphalt material are obtained. This quantitative processing method converts the originally qualitative image information into quantifiable numerical parameters, making the description of the microstructure of the asphalt material more accurate and objective. Establishing the relationship between the characteristic parameters reveals the intrinsic connection between different microscopic characteristics, which makes the understanding of the microstructure of the asphalt material more in-depth. For example, the distribution of pores is affected by aggregate gradation, and the thickness of asphalt film is related to the type of asphalt.

[0010] Preferably, the pore characterization analysis comprises the following steps: Grayscale processing is performed on the three-dimensional structural image of the asphalt material to convert the color image into a grayscale image; The segmentation threshold interval is set to [T1, T2] based on the grayscale image, pixels with grayscale values ​​lower than T1 in the image are identified as pore areas, and pixels with grayscale values ​​higher than T2 are identified as non-pore areas, and the image is converted into a binary image to obtain asphalt pore binary image data; wherein T1 is set to 20% of the average grayscale value of the three-dimensional structure image of the asphalt material, and T2 is set to 80% of the average grayscale value of the three-dimensional structure image of the asphalt material; for pixels with grayscale values ​​between T1 and T2, secondary attribution discrimination is performed according to the grayscale values ​​of their neighboring pixels; Based on the asphalt pore binary image data, the volume corresponding to each pore pixel is calculated using the pixel size and the scanning layer thickness in the three-dimensional structure image of the asphalt material, and the volumes of all pore pixels are accumulated to obtain the total pore volume data of the sample; Extracting the total volume of the asphalt sample according to the three-dimensional structural image of the asphalt material, and dividing the total pore volume data of the sample by the total volume of the asphalt sample to obtain the porosity data of the asphalt material; Calculating the distance from each pore pixel to the nearest non-pore pixel according to the asphalt pore binary image data, thereby obtaining pore size distribution data; Extracting the central skeleton line of the pore area based on the asphalt pore binary image data, and defining the connected skeleton line segments as a connected pore cluster; The number of connected paths and the average path length of the connected pore clusters are counted to obtain data characterizing pore size connectivity.

[0011] The present invention grayscales the three-dimensional structure image and sets the threshold interval for binarization processing, which can effectively identify and segment the pore area, laying the foundation for the subsequent pore feature analysis. Compared with simple threshold segmentation, the introduction of secondary attribution discrimination can more accurately process pixels whose grayscale values ​​are within the threshold interval, reduce misjudgment, and improve segmentation accuracy, thereby obtaining more accurate pore binary image data. Based on the pore binary image data and image pixel size and scanning layer thickness information, the volume corresponding to each pore pixel is calculated and accumulated, and the total pore volume of the sample can be accurately calculated, and then the porosity data can be obtained, which can more accurately reflect the characteristics of the three-dimensional pore structure. By calculating the distance from each pore pixel to the nearest non-pore pixel, the pore size distribution data can be obtained, revealing the changing law of pore size and its influence on material properties. By extracting the central skeleton line of the pore area and counting the number of connected paths and the average path length, the pore connectivity can be effectively characterized.

[0012] Preferably, the aggregate profile feature analysis comprises the following steps: The Canny edge detection algorithm is used to detect the edge of aggregate particles in the surface morphology image of the asphalt material to obtain the edge data of asphalt aggregate particles; wherein the standard deviation σ of the Gaussian filter is set to 1.5, the high threshold is 80% of the maximum gray value of the three-dimensional structure image of the asphalt material, and the low threshold is 40% of the maximum gray value of the three-dimensional structure image of the asphalt material; Tracking the closed contour of aggregate particles based on the edge data of the asphalt aggregate particles; Performing morphological analysis based on the closed contours of the aggregate particles and determining the shapes of the aggregate particles to obtain aggregate shape data; Fitting the minimum circumscribed rectangle of each aggregate particle according to the closed contour line of the aggregate particle, calculating the angle between the major axis direction of the rectangle and the preset reference coordinate system, and obtaining aggregate direction data; The area and equivalent diameter of each aggregate particle are calculated according to the closed contour line of the aggregate particle to obtain aggregate size distribution data.

[0013] The present invention uses the Canny edge detection algorithm to perform edge detection on the surface morphology image of the asphalt material and extract the edge data of the aggregate particles. The Canny algorithm is selected, and the appropriate Gaussian filter standard deviation and high and low thresholds are set to ensure the accuracy and efficiency of edge detection, effectively reduce the influence of noise, and accurately identify the outline of the aggregate particles. By tracking the closed contour of the aggregate particles, the complete segmentation and extraction of the aggregate particles are achieved. Based on the morphological analysis of the closed contour, the shape of the aggregate particles can be determined, so as to obtain the aggregate shape data. The shape of the aggregate has an important influence on the grading design, density, internal friction angle, etc. of the asphalt mixture, and thus affects its deformation resistance. In addition, by fitting the minimum circumscribed rectangle of each aggregate particle and calculating the angle between the major axis direction of the rectangle and the preset reference coordinate system, the aggregate direction data can be obtained. The direction of the aggregate has an important influence on the mechanical properties and anisotropy of the asphalt mixture. For example, the directional arrangement of the aggregate will affect the crack resistance and fatigue resistance of the mixture.

[0014] Preferably, the asphalt interface state analysis comprises the following steps: Segment the asphalt film and the aggregate area according to the asphalt material surface morphology image and the asphalt material three-dimensional structure image; Performing three-phase contact point analysis according to the segmented asphalt film area and the segmented aggregate area to obtain contact characteristic data of the asphalt sample; The interface transition region distance is set according to the contact characteristic data of the asphalt sample to determine the interface transition region between the aggregate and the asphalt film; Taking the aggregate particle closed contour line as a reference line, detecting the vertical distance from the reference line to the interface transition area as the asphalt film thickness at the position; Calculating the film thickness average, standard deviation and coefficient of variation according to the asphalt film thickness, and evaluating the uniformity of the asphalt film to obtain asphalt film uniformity evaluation data; The roughness state of the interface region is analyzed based on the surface roughness image of the asphalt material to obtain the asphalt film-aggregate interface bonding state data.

[0015] The present invention segments the asphalt film and aggregate area according to the surface morphology image and the three-dimensional structure image, laying a foundation for the subsequent asphalt film characteristic analysis. Accurate segmentation results can ensure the reliability and accuracy of subsequent analysis. Based on the segmentation results, a three-phase contact point analysis is performed to obtain the contact characteristic data between the three phases of asphalt, aggregate and pore. Through the three-phase contact point analysis, the contact characteristic data of the asphalt sample is obtained. The three-phase contact point refers to the point where the asphalt film, aggregate particles and pores intersect. The position and number of these points reflect the contact between the asphalt film and the aggregate, as well as the influence of the pores on the bonding. Further, according to the contact characteristic data, the interface transition area between the aggregate and the asphalt film is determined. The interface transition area refers to a region of property transition between the asphalt film and the aggregate. In this region, the properties of the asphalt film are affected by the surface properties of the aggregate, thereby changing the performance of the asphalt film. Taking the closed contour line of the aggregate particles as the baseline, the vertical distance from the baseline to the interface transition area is detected, which is used as the asphalt film thickness at this position. This method can accurately quantify the thickness distribution of the asphalt film. The uniformity of the asphalt film was quantitatively evaluated by calculating the mean, standard deviation and coefficient of variation of the film thickness and conducting an asphalt film uniformity assessment.

[0016] Preferably, the interface region roughness analysis comprises the following steps: Performing image mapping between the interface transition region and the surface roughness image of the asphalt material; Divide the roughness value of the interface transition area by the roughness value of the segmented aggregate area, and then divide the result by the roughness value of the segmented aggregate area to obtain the roughness change rate of the interface area; Set the roughness threshold and asphalt film thickness threshold; If the roughness change rate of the interface area is greater than the roughness threshold, and the asphalt film thickness is greater than the asphalt film thickness threshold, it is determined that the interface bonding state between the asphalt film and the aggregate is good, otherwise it is determined that the interface bonding state is poor, so as to obtain the asphalt film-aggregate interface bonding state data.

[0017] The present invention makes a difference between the roughness value of the interface transition area and the roughness value of the divided aggregate area, and then divides it by the roughness value of the divided aggregate area to obtain the roughness change rate of the interface area. The roughness change rate reflects the change in roughness relative to the aggregate surface in the interface transition area. A high roughness change rate usually means that the asphalt film fills the unevenness of the aggregate surface well, forming a tighter bond. Setting a roughness threshold and an asphalt film thickness threshold provides a standard for evaluating the interface bonding state. Comparing the interface area roughness change rate with the set threshold, combined with the judgment of the asphalt film thickness, the quality of the interface bonding can be comprehensively evaluated. When the interface area roughness change rate is greater than the roughness threshold, and the asphalt film thickness is greater than the asphalt film thickness threshold, it is determined that the asphalt film and the aggregate interface bonding state is good. This indicates that the asphalt film can wet and fill the aggregate surface well, forming a good physical bond and mechanical interlocking. Otherwise, it is determined that the interface bonding state is poor.

[0018] Preferably, the three-phase contact point analysis comprises the following steps: Superimposing the edges of the segmented asphalt film area and the segmented aggregate area, and comparing the edge coordinate information, screening out pixel pairs whose distance is less than a preset contact threshold, and using these pixel pairs as candidate contact points; Calculating the grayscale value change rate of pixels within a neighborhood radius of the candidate contact point, and if the grayscale value changes in at least three directions within the neighborhood of the candidate contact point are greater than a preset grayscale value change threshold, determining that the candidate contact point is a three-phase contact point; Fitting the tangent line of the asphalt film at the contact point with the three-phase contact point as the center to obtain the asphalt film tangent line fitting data; Calculating the normal direction of the aggregate surface at the three-phase contact point, and calculating the contact angle according to the asphalt film tangent fitting data to obtain the asphalt film contact angle; The effective angle is screened according to the contact angle of the asphalt film, and the average contact angle is calculated to obtain the contact characteristic data of the asphalt sample.

[0019] The present invention screens out pixel pairs whose distance is less than a preset contact threshold by edge superposition and compares edge coordinate information at the same time, and uses these pixel pairs as candidate contact points. This is an efficient and automated candidate contact point identification method that can quickly lock the potential three-phase contact point position. By judging whether there are grayscale value changes in at least three directions in the neighborhood that are greater than the preset grayscale value change threshold, pseudo contact points can be effectively excluded to ensure the accuracy of the analysis. After determining the three-phase contact point, the tangent of the asphalt film at the contact point is fitted with the three-phase contact point as the center. The fitting of the tangent provides geometric information of the asphalt film at the contact point, laying the foundation for the subsequent calculation of the contact angle. Subsequently, the normal direction of the aggregate surface at the three-phase contact point is calculated, and the contact angle is calculated based on the asphalt film tangent fitting data. By screening the effective angle and calculating the average contact angle, more representative asphalt sample contact feature data can be obtained, avoiding the influence of the randomness of a single contact angle on the analysis results. The size of the contact angle directly affects the properties of the interface transition area. Good wettability usually means that the interface transition area is more uniform and more tightly bonded.

[0020] Preferably, the asphalt material database management system is created by comprising the following steps: Defining preparation parameter fields for the asphalt material sample, and creating a preparation parameter data table in a relational database; wherein the defined preparation parameter fields include gradation type, modifier type, aging condition, and setting a composite primary key; the composite primary key is composed of gradation type code, modifier type code and aging condition code; Obtaining macroscopic mechanical performance data of asphalt materials; wherein the macroscopic mechanical performance data includes dynamic modulus, creep stiffness and fatigue life data; A multidimensional index structure is constructed according to the preparation parameter data table, the macroscopic mechanical property data of the asphalt material and the microscopic characteristic parameters of the asphalt material, and the preparation parameters are used as the primary screening index, the microscopic characteristic parameters are used as the secondary screening index, and the macroscopic mechanical properties are used as the tertiary screening index, thereby obtaining the asphalt multidimensional index structure; An asphalt material database management system is established for the asphalt material samples based on the asphalt multidimensional index structure.

[0021] The present invention constructs an efficient and flexible database platform, realizes the multi-dimensional association and query of preparation parameters, micro-characteristic parameters and macro-performance data, and greatly improves the research and application efficiency of asphalt materials. The process first defines the preparation parameter field of the asphalt material sample and creates a preparation parameter data table in the relational database. By defining key parameters such as gradation type, modifier type, aging conditions, and setting a composite primary key, the uniqueness and association of the data are ensured. Constructing a multi-dimensional index structure is a key step of the present invention. The preparation parameters, micro-characteristic parameters and macro-mechanical performance data are used as the first, second and third level screening indexes respectively, which can realize the rapid retrieval of data. When the user queries, the database can quickly locate the data that meets the conditions according to the index, avoiding the full table scan, thereby significantly improving the query efficiency. The design of this multi-dimensional index structure fully considers the diversity and complexity of asphalt material data and can meet the user's multi-faceted query needs. Based on the multi-dimensional index structure of asphalt, an asphalt material database management system is created. This system can efficiently store, manage and query a large amount of asphalt material data, and supports complex queries with multiple conditions.

[0022] Preferably, the multidimensional index structure construction includes the following steps: Constructing a B+ tree index for the preparation parameter data table, with gradation type, modifier type and aging condition as joint keys; An R-tree index is constructed for the microscopic characteristic parameters of the asphalt material, with porosity, aggregate size distribution and asphalt film thickness as spatial dimensions; Constructing a hash index for the macroscopic mechanical property data of the asphalt material, with dynamic modulus, creep stiffness and fatigue life as hash keys; The three-level indexes are integrated into a logical association table through a database view.

[0023] The present invention constructs a B+ tree index for the preparation parameter data table, and uses the grading type, modifier type and aging condition as joint keys, which can efficiently support queries based on the preparation parameters. The B+ tree index is suitable for equivalent query and range query, and can quickly locate records that meet the conditions, especially when there are many types of preparation parameters and a large amount of data, the advantage is more obvious. An R-tree index is constructed for the microscopic characteristic parameters of asphalt materials, and porosity, aggregate size distribution and asphalt film thickness are used as spatial dimensions, which can efficiently support range queries based on microscopic characteristic parameters. The R-tree index is suitable for spatial data query, and can quickly find data within a specific spatial range, such as finding asphalt materials with porosity within a certain range. A hash index is constructed for the macroscopic mechanical properties data of asphalt materials, and dynamic modulus, creep stiffness and fatigue life are used as hash keys, which can efficiently support equivalent queries based on macroscopic mechanical properties data. The hash index is suitable for equivalent query, and can quickly locate records with specific macroscopic mechanical properties values, such as finding asphalt materials with dynamic modulus equal to a certain specific value. The three-level index is integrated into a logical association table through a database view. A view is a virtual table that associates and integrates data from multiple tables, allowing users to query data from multiple tables as if they were querying one table. Through database views, preparation parameters, microscopic characteristic parameters, and macroscopic mechanical properties data can be logically associated to achieve multi-dimensional complex queries. Users can perform joint queries based on preparation parameters, microscopic characteristics, and macroscopic properties to find asphalt materials that meet specific requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A schematic flow chart of the steps of the method for establishing an asphalt material database management system of the present invention; Figure 2 A schematic flow chart of the material imaging scanning implementation steps in the method for establishing an asphalt material database management system of the present invention; Figure 3 A schematic flow chart of the implementation steps of creating an asphalt material database in the asphalt material database management system establishment method of the present invention; The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0025] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0026] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0027] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0028] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for establishing an asphalt material database management system, which specifically includes the following steps: Using a scanning electron microscope, an atomic force microscope and an X-ray tomography device, imaging and scanning are performed on asphalt material samples of different gradations, different modifier types and different aging states, respectively, to obtain multi-scale asphalt material microscopic image data; wherein the multi-scale asphalt material microscopic image data includes an asphalt material surface morphology image, an asphalt material surface roughness image and an asphalt material three-dimensional structure image; Performing denoising, contrast enhancement and geometric distortion correction preprocessing operations on the multi-scale asphalt material microscopic image data; Analyzing the microscopic characteristics of the material sample according to the pre-processed multi-scale asphalt material microscopic image data to obtain the microscopic characteristic parameters of the asphalt material; An asphalt material database management system is created for the microscopic characteristic parameters of the asphalt material based on a relational database management system; wherein the asphalt material database management system supports multi-dimensional queries based on preparation parameters, microscopic characteristic parameters and macroscopic performance data, and outputs asphalt material data that meets the conditions.

[0029] In an embodiment of the present invention, the method for establishing the asphalt material database management system specifically includes the following steps: Step S1: using a scanning electron microscope, an atomic force microscope and an X-ray tomography device, respectively, to perform imaging scans on asphalt material samples of different gradations, different modifier types and different aging states, to obtain multi-scale asphalt material microscopic image data; wherein the multi-scale asphalt material microscopic image data includes an asphalt material surface morphology image, an asphalt material surface roughness image and an asphalt material three-dimensional structure image; In the embodiment of the present invention, for example, three asphalt mixture samples with different gradations are prepared in advance, namely AC-13, AC-20 and AC-25, and each gradation is modified by two different types of modifiers SBS and SBR, and samples in three states of unaged, short-term aged and long-term aged are prepared respectively. The surface morphology of the asphalt sample is imaged by a scanning electron microscope (SEM), the acceleration voltage is set to 15kV, the magnifications are 500 times, 1000 times and 2000 times, respectively, to obtain the surface morphology images of the asphalt material at different magnifications. The surface of the asphalt sample is scanned by an atomic force microscope (AFM), the scanning range is set to 5μm×5μm, the scanning rate is set to 1Hz, the surface roughness image data of the asphalt material is obtained, and the roughness parameters of the sample surface, such as root mean square roughness and average roughness, are calculated. The asphalt samples were scanned in three dimensions using X-ray tomography (X-CT) equipment. The scanning voltage was set to 120 kV, the scanning current was set to 100 μA, and the scanning resolution was set to 5 μm. The three-dimensional structural image of the asphalt material was reconstructed, and the porosity and pore size distribution of the three-dimensional image were analyzed to obtain the internal structural characteristic parameters of the asphalt material. All image data were saved in TIFF format, and the corresponding sample information was recorded, including gradation type, modifier type, and aging state.

[0030] Step S2: performing denoising, contrast enhancement and geometric distortion correction preprocessing operations on the multi-scale asphalt material microscopic image data; In an embodiment of the present invention, the acquired SEM, AFM and X-CT image data are imported into ImageJ software for preprocessing. First, the median filtering algorithm is used to denoise the image to remove random noise in the image, and the filter window size is set to 3×3 pixels. Then, the histogram equalization method is used to enhance the image contrast, improve the image clarity and the visualization of detail information. In view of the geometric distortion existing in the SEM image, the polynomial fitting method is used to correct the geometric distortion to ensure the geometric accuracy of the image. For X-CT images, the grayscale threshold segmentation method is used to distinguish asphalt and pores, and three-dimensional reconstruction is performed to obtain the three-dimensional structure of the asphalt material. All preprocessed image data are saved in TIFF format, and the corresponding preprocessing parameters are recorded.

[0031] Step S3: analyzing the microscopic characteristics of the material sample according to the pre-processed multi-scale asphalt material microscopic image data to obtain microscopic characteristic parameters of the asphalt material; In the embodiment of the present invention, the pre-processed SEM image is analyzed by ImageJ software to extract the surface morphology characteristic parameters of the asphalt material, such as the size, distribution and void morphology of asphaltene particles. The pre-processed AFM image is analyzed by Gwyddion AFM image analysis software to calculate the surface roughness parameters of the asphalt material. The pre-processed X-CT three-dimensional image is analyzed by VGStudioMAX software to extract the three-dimensional structural characteristic parameters of the asphalt material, such as porosity, pore connectivity, pore size distribution and skeleton structure.

[0032] Step S4: creating an asphalt material database management system for the microscopic characteristic parameters of the asphalt material based on a relational database management system; wherein the asphalt material database management system supports multi-dimensional queries based on preparation parameters, microscopic characteristic parameters and macroscopic performance data, and outputs asphalt material data that meets the conditions.

[0033] In an embodiment of the present invention, a MySQL relational database management system is used to create an asphalt material database. The database contains multiple data tables, such as a "sample information table", a "SEM image data table", a "AFM image data table", a "X-CT image data table" and a "microscopic characteristic parameter table". The "sample information table" is used to store the preparation parameters of the asphalt sample, including gradation type, modifier type, aging state, etc. The "SEM image data table", "AFM image data table" and "X-CT image data table" are respectively used to store the corresponding image data file path and related imaging parameters. The "microscopic characteristic parameter table" is used to store the microscopic characteristic parameters extracted from the image analysis, such as asphaltene particle size, surface roughness, porosity, etc. The association relationship is established between each data table through the sample ID. The database management system provides a user-friendly graphical interface and supports multi-dimensional queries based on preparation parameters, microscopic characteristic parameters and macroscopic performance data. For example, the user can enter a specific gradation type, modifier type and aging state to query the microscopic characteristic parameters and image data of the asphalt sample that meets the conditions. The user can also query the asphalt sample information that meets the conditions according to the porosity range. The database management system will output asphalt material data that meets the conditions, including sample information, image data, and microscopic characteristic parameters, and display them in the form of tables or charts.

[0034] Preferably, the imaging scan specifically comprises the following steps: According to the preset gradation range, modifier type and aging conditions, asphalt material samples with different gradations, different modifier types and different aging conditions are prepared, and the asphalt material samples are subjected to pretreatment operations such as cutting and polishing to obtain sample surfaces that meet the test requirements; Fixing the pretreated asphalt material sample on a sample stage of a scanning electron microscope, an atomic force microscope and an X-ray tomography device, and determining at least 5 different imaging areas according to the sample size and the target magnification; Start a scanning electron microscope, an atomic force microscope and an X-ray tomography device, scan the imaging areas one by one, and save the scanning image data of each area, to obtain a surface morphology image of the asphalt material, a surface roughness image of the asphalt material and a three-dimensional structure image of the asphalt material respectively; wherein, the surface morphology image of the asphalt material is obtained by a scanning electron microscope; the surface roughness image of the asphalt material is obtained by an atomic force microscope; and the three-dimensional structure image of the asphalt material is obtained by an X-ray tomography device.

[0035] As an example of the present invention, refer to Figure 2 As shown, it is a schematic diagram of the process flow of material imaging scanning implementation steps. In an embodiment of the present invention, the imaging scanning specifically includes the following steps: Step S11: preparing asphalt material samples with different gradations, different modifier types and different aging conditions according to the preset gradation range, modifier type and aging conditions, and performing pretreatment operations such as cutting and polishing on the asphalt material samples to obtain sample surfaces that meet the test requirements; In the embodiment of the present invention, according to the preset gradation range (for example, AC-13, AC-20, AC-25), three aggregates with different gradations are selected, and the optimal asphalt content is determined according to the Marshall mix design method. Two different types of modifiers, such as styrene-butadiene-styrene block copolymer (SBS) and styrene-butadiene rubber (SBR), are added to the base asphalt in three proportions of 3%, 5% and 7% of the asphalt mass to prepare modified asphalt. The aggregate and the modified asphalt are mixed according to the designed mix ratio, and the asphalt mixture specimens are prepared by a rotary compactor, and the specimen size is 100mm in diameter and 63.5mm in height. In order to simulate different aging states, some specimens are placed in a rotary film oven for short-term aging (85°C, 5 days) and long-term aging (100°C, 5 days). All prepared asphalt mixture specimens are cut into 10mm×10mm×10mm cubes using a diamond cutter. Then, the cut sample blocks were ground and polished step by step using 300 mesh, 600 mesh, 1000 mesh and 2000 mesh sandpaper in sequence until a smooth and flat sample surface was obtained to meet the requirements of subsequent microscopic tests. During the polishing process, coolant was used to cool the sample to prevent the asphalt from softening and deforming. Finally, the polished sample was cleaned with anhydrous ethanol and dried with compressed air to avoid contamination of the sample surface.

[0036] Step S12: fixing the pretreated asphalt material sample on a sample stage of a scanning electron microscope, an atomic force microscope and an X-ray tomography device, and determining at least five different imaging areas according to the sample size and the target magnification; In an embodiment of the present invention, for SEM testing, the pretreated asphalt material sample is fixed on an aluminum sample stage with conductive glue, and a thin gold film is sprayed on the sample surface using an ion sputtering device to improve the conductivity of the sample and prevent charging effects. For AFM testing, the sample is fixed on the AFM sample stage with double-sided tape. For X-CT testing, the sample is placed directly on the sample rotating table of the X-CT device. According to the sample size (10mm×10mm×10mm) and the target magnification, at least 5 different imaging areas are selected on each sample surface. When selecting the imaging area, consider the representativeness of the sample and avoid selecting edges or areas with obvious defects. For SEM testing, the target magnifications selected are 500 times, 1000 times, and 2000 times, respectively. For AFM testing, a scanning range of 5μm×5μm is selected. For X-CT testing, a three-dimensional scanning area that includes the entire sample is selected.

[0037] Step S13: Start the scanning electron microscope, atomic force microscope and X-ray tomography equipment, scan the imaging areas one by one, and save the scanning image data of each area, to obtain the surface morphology image of the asphalt material, the surface roughness image of the asphalt material and the three-dimensional structure image of the asphalt material respectively; wherein, the surface morphology image of the asphalt material is obtained by the scanning electron microscope; the surface roughness image of the asphalt material is obtained by the atomic force microscope; and the three-dimensional structure image of the asphalt material is obtained by the X-ray tomography equipment.

[0038] In an embodiment of the present invention, a scanning electron microscope (SEM) is started, and the acceleration voltage is set to 15kV and the working distance is 10mm. According to the predetermined imaging area and magnification, each imaging area is scanned one by one, and the scanned image data of each area is saved to obtain a surface morphology image of the asphalt material, and the image format is TIFF. An atomic force microscope (AFM) is started, the tapping mode is selected, the scanning rate is set to 1Hz, and the scanning range is 5μm×5μm. According to the predetermined imaging area, each imaging area is scanned one by one, and the scanned image data of each area is saved to obtain a surface roughness image of the asphalt material, and the image format is TIFF. An X-ray tomography (X-CT) device is started, and the scanning voltage is set to 120kV, the scanning current is set to 100μA, and the scanning resolution is set to 5μm. A predetermined sample is scanned in three dimensions, and the scanning data is saved. The scanning data is reconstructed in three dimensions using X-CT image reconstruction software to obtain a three-dimensional structural image of the asphalt material, and the image format is DICOM or TIFF. All image data are named and stored according to the sample number, imaging area and magnification.

[0039] Preferably, the microscopic characteristic analysis of the material sample specifically comprises the following steps: Identifying pore areas according to the three-dimensional structural image of the asphalt material, and performing pore characteristic analysis to obtain pore characteristic data; Based on the surface morphology image of the asphalt material and the three-dimensional structure image of the asphalt material, the aggregate particle contour is identified, and the aggregate contour feature analysis is performed to obtain aggregate feature data; The asphalt film is segmented according to the asphalt material surface morphology image, and the asphalt interface state is analyzed through the asphalt material surface roughness image to obtain the asphalt film characteristic data; The pore characteristic data, aggregate characteristic data and asphalt film characteristic data are quantified, and the relationship between the characteristic parameters is established to obtain the microscopic characteristic parameters of the asphalt material.

[0040] In an embodiment of the present invention, VGStudio MAX software is used to identify the pore area of ​​the pre-treated three-dimensional structural image (X-CT image) of the asphalt material. First, the image is binarized by setting a grayscale threshold to distinguish the pore area from the asphalt matrix. The selection of the threshold can be automatically determined by grayscale histogram analysis or Otsu method to ensure accurate identification of the pore area. Then, morphological operations such as corrosion and expansion operations are performed on the binarized image to remove noise and isolated pixels in the image, and connect the broken pore areas to make the pore area more complete. Next, the identified pore area is three-dimensionally reconstructed to obtain the three-dimensional structural information of the pores. Based on the reconstructed three-dimensional pore structure, pore characteristic data such as porosity (the ratio of pore volume to total volume), pore volume distribution (the volume proportion of pores of different sizes), pore surface area, pore connectivity (the proportion of interconnected pores), average pore diameter, and pore shape factor (an indicator describing the complexity of the pore shape, such as sphericity and circularity) can be calculated. The aggregate particle contours are identified and analyzed by combining the surface morphology image (SEM image) and the three-dimensional structure image (X-CT image) of the asphalt material. First, edge detection is performed on the SEM image, such as the Canny edge detection algorithm, to extract the edge information of the aggregate particles. Then, the aggregate particles are segmented from the asphalt matrix using image segmentation techniques, such as the watershed algorithm. For X-CT images, similar image processing methods can be used, or the grayscale threshold segmentation method can be used to distinguish the aggregate particles from the asphalt matrix and pores. After the aggregate particle contours are identified, it is necessary to perform aggregate contour feature analysis to extract aggregate feature data. Aggregate feature data includes aggregate particle size, aggregate shape (such as aggregate aspect ratio, aggregate roundness, aggregate angularity), aggregate quantity, aggregate distribution (such as aggregate spacing, aggregate direction), aggregate surface texture, etc. Aggregate particle size can be measured directly from the segmented image. Aggregate shape can be quantified using shape factors. Aggregate distribution can be obtained by calculating the centroid coordinates of the aggregate and then analyzing the distribution of the centroid coordinates. Aggregate surface texture can be analyzed using methods such as gray-level co-occurrence matrix (GLCM). The SEM image needs to be segmented to separate the asphalt film from the aggregate and pore areas. Common segmentation methods include threshold segmentation based on grayscale value, edge-based region growing, and segmentation methods combined with morphological operations. Since the grayscale value of the asphalt film is usually between the aggregate and the pores, a suitable threshold range can be set to segment the asphalt film. Then, the surface roughness image (AFM image) of the asphalt material is used to analyze the interface state of the asphalt film. The roughness parameters of the asphalt film surface, such as root mean square roughness (Rq), average roughness (Ra), and peak-to-valley value (Rpv), are calculated. In addition, the thickness distribution, uniformity, and adhesion of the asphalt film to the aggregate particles can also be analyzed.The acquired pore characteristic data, aggregate characteristic data and asphalt film characteristic data are quantified. For example, the pore size distribution is converted into discrete numerical intervals and corresponding volume percentages; the aggregate shape factor is converted into specific numerical values; the asphalt film thickness distribution is converted into average thickness and standard deviation, etc. Then, statistical methods, such as correlation analysis and regression analysis, are used to establish the relationship between characteristic parameters. For example, the Pearson correlation coefficient or Spearman rank correlation coefficient between different characteristic parameters is calculated to evaluate the linear correlation or rank correlation between them. For example, the correlation coefficient between porosity and asphalt film thickness can be calculated to analyze the effect of porosity changes on asphalt film thickness. The correlation coefficient between aggregate shape factor and asphalt film surface roughness can also be calculated to analyze the effect of aggregate shape on asphalt film microstructure. For example, a regression model between porosity and asphalt mixture fatigue resistance can be established to predict the fatigue life of asphalt mixtures under different porosities. A regression model between aggregate characteristic parameters (such as size distribution, shape factor) and asphalt mixture mechanical properties (such as compressive strength, tensile strength) can also be established to analyze the effect of aggregate characteristics on asphalt mixture mechanical properties. For example, asphalt samples with different gradations, different modifier types and different aging states can be clustered according to pore characteristics, aggregate characteristics and asphalt film characteristics to find sample groups with similar microstructures.

[0041] Preferably, the pore characteristic analysis specifically comprises the following steps: Grayscale processing is performed on the three-dimensional structural image of the asphalt material to convert the color image into a grayscale image; The segmentation threshold interval is set to [T1, T2] based on the grayscale image, pixels with grayscale values ​​lower than T1 in the image are identified as pore areas, and pixels with grayscale values ​​higher than T2 are identified as non-pore areas, and the image is converted into a binary image to obtain asphalt pore binary image data; wherein T1 is set to 20% of the average grayscale value of the three-dimensional structure image of the asphalt material, and T2 is set to 80% of the average grayscale value of the three-dimensional structure image of the asphalt material; for pixels with grayscale values ​​between T1 and T2, secondary attribution discrimination is performed according to the grayscale values ​​of their neighboring pixels; Based on the asphalt pore binary image data, the volume corresponding to each pore pixel is calculated using the pixel size and the scanning layer thickness in the three-dimensional structure image of the asphalt material, and the volumes of all pore pixels are accumulated to obtain the total pore volume data of the sample; Extracting the total volume of the asphalt sample according to the three-dimensional structural image of the asphalt material, and dividing the total pore volume data of the sample by the total volume of the asphalt sample to obtain the porosity data of the asphalt material; Calculating the distance from each pore pixel to the nearest non-pore pixel according to the asphalt pore binary image data, thereby obtaining pore size distribution data; Extracting the central skeleton line of the pore area based on the asphalt pore binary image data, and defining the connected skeleton line segments as a connected pore cluster; The number of connected paths and the average path length of the connected pore clusters are counted to obtain data characterizing pore size connectivity.

[0042] In an embodiment of the present invention, a three-dimensional structural image of an asphalt material obtained by X-ray tomography (X-CT) (usually stored in the form of a DICOM sequence or a multi-layer TIFF file) is loaded into a professional image processing software, such as ImageJ (with a Fiji plug-in package), Avizo or VGStudio MAX. These softwares all provide functions for processing and analyzing three-dimensional image data. If the original image is a color image (e.g., RGB format), it needs to be converted into a grayscale image. Traverse all pixels of the three-dimensional grayscale image, calculate the sum of the grayscale values ​​of all pixels, and then divide it by the total number of pixels to obtain the average grayscale value. Then, 20% and 80% of the average grayscale value are calculated respectively to obtain T1 and T2. For each pixel in the image, the following judgment is made: if the pixel grayscale value is less than T1, the pixel is marked as a pore pixel, and the corresponding pixel value in the binary image is set to 1 (or 255, depending on the specific software and data format). If the pixel grayscale value is greater than T2, the pixel is marked as a non-pore pixel, and the corresponding pixel value in the binary image is set to 0. If the pixel grayscale value is between T1 and T2, a secondary attribution discrimination is required. With the pixel as the center, select a 3x3x3 or 5x5x5 cubic neighborhood (in a three-dimensional image). Calculate the grayscale average of all pixels in the neighborhood. If the neighborhood average grayscale value is less than (T1+T2) / 2, the pixel is marked as a pore pixel; otherwise, it is marked as a non-pore pixel. The pixel size and scanning layer thickness of the X-ray tomography device need to be obtained. The pixel size refers to the actual physical size represented by a pixel point in the XCT image, such as 10 microns × 10 microns. The scanning layer thickness refers to the thickness of each layer scanned during the XCT scan, such as 10 microns. A pixel represents a small volume unit in the three-dimensional structure image, and its volume size can be calculated by the pixel size and scanning layer thickness. For the asphalt pore binary image data, the pixel with a grayscale value of 0 represents the pore area. Therefore, it is necessary to traverse the binary image and count the number of pixels with a grayscale value of 0. The statistical method can scan the image row by row or column by column, and accumulate the number of pixels with a grayscale value of 0. Assume that the number of pixels with a grayscale value of 0 obtained by statistics is N. Then, the number of pore pixels N is multiplied by the volume represented by each pixel to obtain the total pore volume data of the sample. The calculation formula is: total pore volume of the sample = N × (pixel size × pixel size × scanning layer thickness). For example, a threshold-based segmentation method. Based on the grayscale image, a threshold is set to identify the pixels in the image with grayscale values ​​higher than the threshold as asphalt samples, and the pixels with grayscale values ​​lower than the threshold are identified as other parts, thereby realizing the segmentation of the asphalt sample. Compare it with the obtained total pore volume data of the sample to calculate the porosity data of the asphalt material. Porosity is defined as the ratio of the total pore volume to the total volume of the sample, usually expressed as a percentage.The calculation formula is: Porosity = (total volume of sample pores / total volume of asphalt sample) × 100%. For each pore pixel (grayscale value is 0) in the binary image of asphalt pores, it is necessary to find the nearest non-pore pixel (grayscale value is 255). Euclidean distance transform is a commonly used distance transform algorithm, which is used to calculate the straight-line distance from each pixel to the nearest non-zero pixel. On the binary image, the Euclidean distance transform calculates the Euclidean distance from each pixel to the nearest non-pore pixel. During the calculation process, each pixel in the image is traversed. For each pixel, the distance from it to all non-pore pixels is calculated, and the smallest distance is taken as the distance from the pixel to the nearest non-pore pixel. The pore area is simplified into a curve located at the center of the pore, retaining the main topological structure information of the pore. Common skeletonization algorithms include thinning algorithm and distance transform skeletonization algorithm. The thinning algorithm peels off the boundary pixels of the pore area layer by layer until only the central skeleton line is left. The distance transform skeletonization algorithm is based on the result of distance transform and extracts the local maximum point as the skeleton point. Select a skeletonization algorithm to process the binary image of asphalt pores. After the algorithm is processed, there will be a skeleton line at the center of the pore area in the obtained image. The skeleton line is composed of a series of pixels, which are connected to form the central structure of the pores. Then, the connected skeleton line segments are defined as a connected pore cluster. The connected domain analysis algorithm can identify connected skeleton line segments. The connected domain analysis algorithm starts from a pixel in the image and recursively marks all pixels connected to the pixel as the same area. If the skeleton line segments are continuous, they belong to the same connected pore cluster. The number of connected paths and the average path length are counted for each connected pore cluster. The number of connected paths refers to the number of paths that can reach each other in the connected pore cluster. The average path length refers to the average length of all paths in the connected pore cluster. The number of connected paths and the average path length can be calculated using graph theory algorithms. Each skeleton line segment can be regarded as a node of the graph. If two skeleton line segments are connected, an edge is established between the nodes. Calculating the number of connected paths is to calculate the number of edges connecting two nodes in the graph. The average path length can be calculated by summing the lengths of all paths and dividing by the number of connected paths.

[0043] Preferably, the aggregate profile feature analysis specifically comprises the following steps: The Canny edge detection algorithm is used to detect the edge of aggregate particles in the surface morphology image of the asphalt material to obtain the edge data of asphalt aggregate particles; wherein the standard deviation σ of the Gaussian filter is set to 1.5, the high threshold is 80% of the maximum gray value of the three-dimensional structure image of the asphalt material, and the low threshold is 40% of the maximum gray value of the three-dimensional structure image of the asphalt material; Tracking the closed contour of aggregate particles based on the edge data of the asphalt aggregate particles; Performing morphological analysis based on the closed contours of the aggregate particles and determining the shapes of the aggregate particles to obtain aggregate shape data; Fitting the minimum circumscribed rectangle of each aggregate particle according to the closed contour line of the aggregate particle, calculating the angle between the major axis direction of the rectangle and the preset reference coordinate system, and obtaining aggregate direction data; The area and equivalent diameter of each aggregate particle are calculated according to the closed contour line of the aggregate particle to obtain aggregate size distribution data.

[0044] In an embodiment of the present invention, the Canny edge detection algorithm is used to process the surface morphology image (SEM image) of the asphalt material, detect the edge of the aggregate particles, and obtain the edge data of the asphalt aggregate particles. The Canny edge detection algorithm is a multi-step image processing algorithm that aims to find the location where the intensity changes dramatically in the image, that is, the edge. The image is smoothed using a Gaussian filter to remove noise. The standard deviation σ of the Gaussian filter is set to 1.5 pixels. The convolution kernel size of the Gaussian filter is usually automatically determined based on the standard deviation, generally (6×σ+1) rounded, which is about 10×10 pixels here. The gradient amplitude and gradient direction of each pixel in the image are calculated. The gradient amplitude represents the speed and intensity of the change of the image gray value, and the gradient direction represents the direction of the change of the image gray value. The gradient amplitude and gradient direction can be calculated using the Sobel operator, the Prewitt operator, or a more advanced gradient operator. These operators are essentially convolution operations on the image, and by differentiating the image, the grayscale change rate of the image in the horizontal and vertical directions is calculated, and then the gradient amplitude and gradient direction are obtained. Use double threshold processing to divide edge pixels into strong edge pixels, weak edge pixels, and non-edge pixels. Set two thresholds, a high threshold and a low threshold. The high threshold is usually greater than the low threshold. If the gradient amplitude of a pixel is greater than the high threshold, it is marked as a strong edge pixel. If the gradient amplitude of a pixel is less than the low threshold, it is marked as a non-edge pixel. If the gradient amplitude of a pixel is between the high threshold and the low threshold, it is marked as a weak edge pixel. Edge connection is performed on weak edge pixels to form a complete edge. The principle of edge connection is that if a weak edge pixel is adjacent to a strong edge pixel, the weak edge pixel is also marked as an edge pixel. In this way, edge breaks caused by noise or uneven illumination can be connected to form a complete edge. Randomly select an edge pixel from the edge data as the starting point. Then, search for the next edge pixel along the direction of the edge. The principle of searching is to find pixels that meet the edge conditions in the neighborhood of the current pixel. The definition of adjacent pixels can use 4 neighborhoods or 8 neighborhoods. Some heuristic rules can be used to connect the edges. For example, the gradient direction of the next pixel should be consistent with the gradient direction of the current pixel. If the next edge pixel is found, add the pixel to the contour line and use it as the new current pixel. Repeat the above process until you return to the starting point or the next edge pixel cannot be found. If you return to the starting point, a closed contour line is formed. If the next edge pixel cannot be found, it means that the edge is broken. In the case of a broken edge, search for the edge pixel closest to the pixel in the neighborhood of the current pixel. If such a pixel is found, connect the two pixels.The purpose of morphological analysis is to extract shape features from the contours of aggregate particles, so as to describe and classify the shapes of aggregate particles. The description of the shapes of aggregate particles can be carried out from multiple aspects, for example: fitting a minimum circumscribed rectangle to the closed contours of aggregate particles, the aspect ratio is the ratio of the long side to the short side of the minimum circumscribed rectangle; roundness is the ratio of the area of ​​the aggregate particle to the square of its perimeter, which can reflect the degree of closeness of the aggregate particle to a circle; angularity is the degree of concavity of the contour of the aggregate particle, which can reflect the degree of sharpness of the aggregate particle. To calculate angularity, the curvature of the contour of the aggregate particle must be calculated first. The curvature can be calculated using a difference-based method; convexity refers to the ratio of the area of ​​the aggregate particle to the area of ​​its convex hull, which can reflect the degree of concavity of the aggregate particle. The convex hull refers to the smallest convex polygon that contains all the pixels of the aggregate particle. Fit a minimum circumscribed rectangle to the closed contour of each aggregate particle. The minimum circumscribed rectangle is the rectangle with the smallest area that contains the contour of the aggregate particle. The minimum circumscribed rectangle of the aggregate particles can be calculated using the rotating calculus algorithm or the least squares method, and the major axis direction of the rectangle can be calculated. Calculate the angle between the major axis direction of the rectangle and the preset reference coordinate system. The choice of the reference coordinate system can be determined according to the actual situation. In the SEM image, the horizontal direction of the image is usually selected as the reference coordinate system. Calculate the angle between the major axis direction of the rectangle and the horizontal direction. The inverse tangent function arctan() can be used to calculate the angle. Statistic the distribution of the aggregate particle direction. The aggregate direction can be divided into several intervals, for example, 0-10 degrees, 10-20 degrees, etc. Then, count the number of aggregate particles falling in each interval, and plot the statistical results into a histogram. For each closed contour line of the aggregate particle, count the number of pixels located inside the contour line. The area of ​​each pixel can be obtained based on the pixel size of the SEM image. For example, if the pixel size of the SEM image is 10 microns × 10 microns, then the area of ​​each pixel is 100 square microns. The area of ​​the aggregate particle is equal to the number of internal pixels multiplied by the area of ​​the pixel. Calculate the equivalent diameter of the aggregate particle. The equivalent diameter refers to the diameter of a circle with the same area as the aggregate particle.

[0045] Preferably, the asphalt interface state analysis specifically includes the following steps: Segment the asphalt film and the aggregate area according to the asphalt material surface morphology image and the asphalt material three-dimensional structure image; Performing three-phase contact point analysis according to the segmented asphalt film area and the segmented aggregate area to obtain contact characteristic data of the asphalt sample; The interface transition region distance is set according to the contact characteristic data of the asphalt sample to determine the interface transition region between the aggregate and the asphalt film; Taking the aggregate particle closed contour line as a reference line, detecting the vertical distance from the reference line to the interface transition area as the asphalt film thickness at the position; Calculating the film thickness average, standard deviation and coefficient of variation according to the asphalt film thickness, and evaluating the uniformity of the asphalt film to obtain asphalt film uniformity evaluation data; The roughness state of the interface region is analyzed based on the surface roughness image of the asphalt material to obtain the asphalt film-aggregate interface bonding state data.

[0046] In an embodiment of the present invention, the SEM image and the XCT image are preprocessed to improve the accuracy of segmentation. The preprocessing operation includes denoising, contrast enhancement and image correction. Denoising can use methods such as mean filtering, median filtering or non-local mean filtering to remove noise in the image. Contrast enhancement can use methods such as histogram equalization to improve the visual effect of the image. On the binary images of the segmented asphalt film area and the aggregate area, the three-phase contact point is detected. The three-phase contact point is defined as the intersection point of the three areas of asphalt film, aggregate and pore. By traversing the pixel points of the binary image, it can be judged whether each pixel point meets the definition of the three-phase contact point. The judgment method can be based on neighborhood analysis. For example, a 3x3 neighborhood is selected. If there are asphalt film pixels, aggregate pixels and pore pixels in the neighborhood, the pixel point is considered to be a three-phase contact point. In order to avoid misjudgment, a certain tolerance can be set. For example, it can be allowed that a phase is missing in the neighborhood and it is also identified as a three-phase contact point. The number of three-phase contact points and the distribution of three-phase contact points are calculated. The number of three-phase contact points refers to the total number of all pixels in the image that are identified as three-phase contact points. The distribution of three-phase contact points can be described by counting their position coordinates. For example, the coordinates of the center of gravity of the three-phase contact points can be calculated and drawn in the image. The image can also be divided into several regions, and the number of three-phase contact points in each region can be counted to analyze the distribution of three-phase contact points in the asphalt mixture. For example, if the three-phase contact points are more densely distributed, the interface transition area is also wider. A mathematical model between contact feature data and the distance of the interface transition area can be established. For example, the distance of the interface transition area can be set to be proportional to the average distance of the three-phase contact points. On the closed contour line of the aggregate particles, a series of points are selected as measurement points. The selection of measurement points can be performed in an equidistant or random manner. For example, a measurement point can be selected at a certain distance on the contour line. The vertical distance from each measurement point to the interface transition area is calculated. The interface transition area refers to the area where the aggregate surface is combined with the asphalt film. According to the set interface transition area distance, the distance to the interface transition area is measured along the direction perpendicular to the contour line with the closed contour line of the aggregate particles as the reference. If the distance of the interface transition area is set to a fixed value, such as 5 microns. For each measurement point on the contour line, move 5 microns outward (i.e., the direction of the asphalt film) in a direction perpendicular to the contour line, and the position after the move is the interface transition area. By measuring the distance from the point to the contour line, the thickness of the asphalt film at the measurement point is obtained. Repeat the above process to measure the thickness of the asphalt film at all measurement points. Perform statistical analysis on the obtained asphalt film thickness measurement data to calculate the mean, standard deviation and coefficient of variation of the asphalt film thickness. For example, the coefficient of variation can be set to be uniform when less than 10%, general when 10%-20%, and non-uniform when greater than 20%. Extract the asphalt film-aggregate interface area from the AFM image. The definition of the interface area depends on the boundary between the asphalt film and the aggregate.Perform a roughness analysis on the interface area. Commonly used roughness parameters include: arithmetic mean roughness, root mean square roughness, and the sum of the maximum peak height and the maximum valley depth of the profile curve. According to the calculated roughness parameters, evaluate the bonding state of the asphalt film-aggregate interface. The magnitude of the roughness is related to the adhesion of the interface. Higher roughness increases the adhesion of the interface, thereby improving the strength and durability of the asphalt mixture. By correlating these roughness parameters with the performance indicators of the asphalt mixture, the relationship between the interface bonding state and the performance can be established. The asphalt film-aggregate interface bonding state data is obtained.

[0047] Preferably, the interface region roughness analysis specifically comprises the following steps: Performing image mapping between the interface transition region and the surface roughness image of the asphalt material; Divide the roughness value of the interface transition area by the roughness value of the segmented aggregate area, and then divide the result by the roughness value of the segmented aggregate area to obtain the roughness change rate of the interface area; Set the roughness threshold and asphalt film thickness threshold; If the roughness change rate of the interface area is greater than the roughness threshold, and the asphalt film thickness is greater than the asphalt film thickness threshold, it is determined that the interface bonding state between the asphalt film and the aggregate is good, otherwise it is determined that the interface bonding state is poor, so as to obtain the asphalt film-aggregate interface bonding state data.

[0048] In an embodiment of the present invention, it is necessary to determine the correspondence between the SEM image and the AFM image. Since the SEM image and the AFM image are obtained on different devices and there are differences in the scanned areas, image registration is required. There are many methods for image registration, and a registration method based on feature points or a registration method based on regions can be used. In the scenario of asphalt mixture, aggregate particles can be selected as feature points, the same aggregate particles in the SEM image and the AFM image can be identified, and the correspondence between these feature points can be established. Using the transformation relationship of image registration, the determined interface transition area is mapped to the AFM image. The interface transition area is usually determined based on the contour lines of the aggregate particles. The contour lines of the aggregate particles are mapped to the AFM image according to the transformation relationship obtained by image registration, and the corresponding aggregate particle contour lines on the AFM image can be obtained. Then, according to the distance setting of the determined interface transition area, the interface transition area is determined on the AFM image. For example, if the distance of the interface transition area is set to 5 microns, then move 5 microns outward (in the direction of the asphalt film) in the direction perpendicular to the contour line of the aggregate particles to obtain the corresponding interface transition area on the AFM image. It is necessary to calculate the roughness value of the segmented aggregate area. The segmented aggregate area refers to the area on the surface of the aggregate particles adjacent to the interface transition area in the AFM image. Extract the roughness value of the segmented aggregate area. The calculation method is the same as that of calculating the roughness value of the interface transition area. Select the same roughness parameter to calculate the roughness value of the segmented aggregate area. Calculate the roughness change rate of the interface area. The calculation formula is: Roughness change rate = (roughness value of the interface transition area - roughness value of the segmented aggregate area) / roughness value of the segmented aggregate area × 100%. Set the roughness threshold. The roughness threshold is used to determine whether the roughness change rate of the interface area is large enough, indicating that the bonding state of the asphalt film and the aggregate is good. The setting of the roughness threshold can be carried out according to experimental results and experience. By preparing asphalt mixture samples with different bonding states, the interface roughness change rate can be measured and its mechanical properties can be tested. Then, according to the mechanical properties, set a suitable roughness threshold. For example, if the roughness change rate is greater than 50%, it is considered that the bonding state between the asphalt film and the aggregate is good. If the roughness change rate is less than 20%, it is considered that the bonding state between the asphalt film and the aggregate is poor. Determine whether the roughness change rate of the interface area is greater than the roughness threshold. If the roughness change rate of the interface area is greater than the roughness threshold, it means that the interface transition area is rougher than the aggregate surface, indicating that the bonding between the asphalt film and the aggregate is better. Then, determine whether the asphalt film thickness is greater than the asphalt film thickness threshold. Asphalt film thickness is an important indicator for evaluating the performance of asphalt mixtures. If the asphalt film thickness is greater than the asphalt film thickness threshold, it means that the asphalt film has sufficient thickness to provide sufficient bonding force and enhance the bonding between the asphalt film and aggregate particles.If the roughness change rate of the interface area is greater than the roughness threshold, and the asphalt film thickness is greater than the asphalt film thickness threshold, it is determined that the interface bonding state between the asphalt film and the aggregate is good. A good bonding state can improve the strength, durability and water damage resistance of the asphalt mixture. Otherwise, it is determined that the interface bonding state between the asphalt film and the aggregate is poor. A poor bonding state will lead to a decrease in the performance of the asphalt mixture.

[0049] Preferably, the three-phase contact point analysis specifically includes the following steps: Superimposing the edges of the segmented asphalt film area and the segmented aggregate area, and comparing the edge coordinate information, screening out pixel pairs whose distance is less than a preset contact threshold, and using these pixel pairs as candidate contact points; Calculating the grayscale value change rate of pixels within a neighborhood radius of the candidate contact point, and if the grayscale value changes in at least three directions within the neighborhood of the candidate contact point are greater than a preset grayscale value change threshold, determining that the candidate contact point is a three-phase contact point; Fitting the tangent line of the asphalt film at the contact point with the three-phase contact point as the center to obtain the asphalt film tangent line fitting data; Calculating the normal direction of the aggregate surface at the three-phase contact point, and calculating the contact angle according to the asphalt film tangent fitting data to obtain the asphalt film contact angle; The effective angle is screened according to the contact angle of the asphalt film, and the average contact angle is calculated to obtain the contact characteristic data of the asphalt sample.

[0050] In an embodiment of the present invention, the edge data of the asphalt film area and the edge data of the aggregate area are superimposed. The superposition operation is to merge the edge data of the asphalt film area and the edge data of the aggregate area together to form a new data set. The coordinate information of the edge data is compared. The merged edge pixels are traversed, and the distances between the asphalt film edge pixels and the aggregate edge pixels are calculated respectively. The distance can be calculated using Euclidean distance, etc. Pixel pairs whose distance is less than a preset contact threshold are screened. The preset contact threshold is an empirical value, which represents the maximum distance between the edge pixels of the two regions that can be considered as contact. The selection of the contact threshold needs to be adjusted according to the actual situation, and can usually be set to a distance of several pixels. For example, if the contact threshold is set to 2 pixels, only pixel pairs with a distance less than 2 pixels are retained. If the distance between two pixels is less than the preset contact threshold, the two pixels are used as candidate contact points. The neighborhood radius needs to be determined. The neighborhood radius refers to selecting a circular or square area with the candidate contact point as the center. The size of the neighborhood radius needs to be adjusted according to the resolution of the image and the actual situation. For example, the neighborhood radius can be set to 3 pixels or 5 pixels. Then, the grayscale value change rate of the pixels in the neighborhood of the candidate contact point is calculated. The grayscale value change rate refers to the speed and direction of the change of the grayscale value of the pixel in the neighborhood. There are many methods to calculate the grayscale value change rate. One method is to calculate the gradient amplitude of the pixels in the neighborhood. The gradient amplitude reflects the magnitude of the grayscale value change. The gradient direction reflects the direction of the grayscale value change. Another method is to calculate the grayscale value difference of the pixels in the neighborhood. The grayscale value difference can reflect the grayscale value difference between adjacent pixels. Set a grayscale value change threshold. The grayscale value change threshold is an empirical value used to determine whether the pixel point belongs to a three-phase contact point. The setting of the grayscale value change threshold needs to be adjusted according to the actual situation. For example, the grayscale value change threshold can be set to 10% or 20% of the grayscale value range according to the grayscale value range of the image. If the grayscale value change in at least three directions in the neighborhood of the candidate contact point is greater than the preset grayscale value change threshold, the candidate contact point is determined to be a three-phase contact point. This means that there is an obvious change in grayscale value around the candidate contact point, indicating that there is an intersection point of the three phases of asphalt film, aggregate and pores. If this condition is met, the candidate contact point can be considered to be a three-phase contact point. The fitting range of the tangent needs to be determined. Since the tangent is a description of the local area, it is necessary to determine the pixel points used for fitting. The fitting range can be selected within the neighborhood centered on the three-phase contact point. The size of the neighborhood needs to be adjusted according to the resolution of the image and the actual situation. Tangent fitting is performed on the pixel points of the asphalt film in the neighborhood. There are many methods for tangent fitting, such as linear fitting, polynomial fitting, etc. Considering the computational efficiency, a linear fitting method can be selected. The linear fitting method is to fit the pixel points of the asphalt film in the neighborhood into a straight line. The parameters of the straight line can be solved using methods such as the least squares method.The slope and intercept of the tangent can be determined by the least squares method. The slope of the tangent can indicate the degree of inclination of the asphalt film at the contact point. The intercept of the tangent can indicate the position of the tangent in the coordinate system. It is necessary to calculate the normal direction of the aggregate surface at the three-phase contact point. The normal direction of the aggregate surface refers to the direction perpendicular to the aggregate surface. The edge pixels of the aggregate at the three-phase contact point can be extracted to calculate the gradient direction of the point and define its normal direction. For a given three-phase contact point, it is necessary to extract the edge information of the point from the aggregate area. The edge information provides the local direction of the aggregate surface at the point. Edge detection operators, such as the Sobel operator, can be used to calculate the gradient direction of the aggregate at the contact point. The gradient direction is the direction with the largest change in grayscale value. The normal direction is perpendicular to the gradient direction, so the normal direction of the aggregate surface at the point can be calculated based on the gradient direction. The contact angle refers to the angle between the tangent direction of the asphalt film and the normal direction of the aggregate surface. The contact angle can be calculated using the vector dot product method. The contact angle can be calculated using the following formula: Contact angle = arccos((normal direction vector · tangent direction vector) / (normal direction vector length × tangent direction vector length)). Range filtering can be used. Set a valid range for the contact angle. For example, set the valid range for the contact angle to 0 degrees to 180 degrees. Remove contact angle values ​​outside this range. You can also use statistical filtering methods. For example, calculate the mean and standard deviation of the contact angle, and remove contact angle values ​​outside the mean ± 3 times the standard deviation.

[0051] Preferably, the asphalt material database management system is created by specifically comprising the following steps: Defining preparation parameter fields for the asphalt material sample, and creating a preparation parameter data table in a relational database; wherein the defined preparation parameter fields include gradation type, modifier type, aging condition, and setting a composite primary key; the composite primary key is composed of gradation type code, modifier type code and aging condition code; Obtaining macroscopic mechanical performance data of asphalt materials; wherein the macroscopic mechanical performance data includes dynamic modulus, creep stiffness and fatigue life data; A multidimensional index structure is constructed according to the preparation parameter data table, the macroscopic mechanical property data of the asphalt material and the microscopic characteristic parameters of the asphalt material, and the preparation parameters are used as the primary screening index, the microscopic characteristic parameters are used as the secondary screening index, and the macroscopic mechanical properties are used as the tertiary screening index, thereby obtaining the asphalt multidimensional index structure; An asphalt material database management system is established for the asphalt material samples based on the asphalt multidimensional index structure.

[0052] As an example of the present invention, refer to Figure 3 FIG. 1 is a flow chart showing the steps for implementing the creation of an asphalt material database. In an embodiment of the present invention, the creation of an asphalt material database specifically includes the following steps: Step S21: defining a preparation parameter field for the asphalt material sample, and creating a preparation parameter data table in a relational database; wherein the defined preparation parameter field includes gradation type, modifier type, aging condition, and setting a composite primary key; the composite primary key is composed of a gradation type code, a modifier type code, and an aging condition code; In the embodiment of the present invention, the preparation parameter field is used to describe the preparation process and material composition of the asphalt material, and these parameters directly affect the performance of the asphalt material. The preparation parameter field includes at least: gradation type, modifier type and aging condition. The gradation type describes the proportion of aggregates of different particle sizes in the asphalt mixture. Industry standards such as AC-13, AC-16 and AC-20 can be used, representing gradation types of different particle size ranges. The modifier type describes the type of modifier used in the asphalt material. The modifier can improve the performance of the asphalt material, such as SBS (styrene-butadiene-styrene block copolymer), SBR (styrene-butadiene rubber), EVA (ethylene-vinyl acetate copolymer), etc. The aging condition describes the degree of aging experienced by the asphalt material during service. The aging conditions include short-term aging and long-term aging. Short-term aging usually simulates the aging of the asphalt material during mixing and paving, and long-term aging usually simulates the aging of the asphalt material during road service. For example, short-term aging can use the rotating thin film oven test (RTFOT), and long-term aging can use the pressure aging autoclave test (PAV). The gradation type code, modifier type code and aging condition code are combined to form a composite primary key. For example, when the gradation type is AC-13, the modifier is SBS, and the aging condition is RTFOT, the composite primary key can be 01-01-01. The composite primary key can ensure that each asphalt material sample has a unique identification.

[0053] Step S22: obtaining macroscopic mechanical property data of asphalt materials; wherein the macroscopic mechanical property data includes dynamic modulus, creep stiffness and fatigue life data; In an embodiment of the present invention, macroscopic mechanical properties data of materials are mined by publicly available literature on experimental data on mechanical properties of asphalt materials. The macroscopic mechanical properties data include dynamic modulus, creep stiffness and fatigue life data. The dynamic modulus describes the stiffness of the asphalt material under cyclic load. The dynamic modulus can be obtained through a dynamic modulus test (for example, a repeated loading creep test or a dynamic bending test). Creep stiffness describes the deformation characteristics of the asphalt material under continuous load. Creep stiffness can be obtained through a creep test. Creep stiffness decreases with time. The creep stiffness can be measured at different temperatures and at different loading times. Fatigue life describes the fatigue resistance of the asphalt material under cyclic load. Fatigue life can be obtained through fatigue testing. Fatigue testing is to apply cyclic loads to asphalt material samples and record their fatigue life at different stress levels.

[0054] Step S23: construct a multidimensional index structure according to the preparation parameter data table, the macroscopic mechanical property data of the asphalt material and the microscopic characteristic parameters of the asphalt material, taking the preparation parameters as the primary screening index, the microscopic characteristic parameters as the secondary screening index, and the macroscopic mechanical property as the tertiary screening index, thereby obtaining the asphalt multidimensional index structure; In an embodiment of the present invention, the preparation parameters are used as the first-level screening index. The preparation parameters include gradation type, modifier type, and aging condition. The purpose of the first-level screening index is to quickly narrow the data range according to the user's screening conditions. For example, if the user wants to query asphalt material samples with a gradation type of AC-13 and a modifier type of SBS, the first-level screening index can be filtered according to the gradation type and the modifier type to narrow the data range. The microscopic characteristic parameters are used as the second-level screening index. The microscopic characteristic parameters describe the microstructural characteristics of the asphalt material, such as porosity, aggregate size, asphalt film thickness, etc. The purpose of the second-level screening index is to further narrow the data range on the basis of the first-level screening index. For example, if the user wants to query asphalt material samples with a porosity of less than 5%, the second-level screening index can be filtered according to the porosity to narrow the data range. The macroscopic mechanical properties are used as the third-level screening index. The macroscopic mechanical properties describe the mechanical properties of the asphalt material, such as dynamic modulus, creep stiffness, fatigue life, etc. The purpose of the third-level screening index is to finally determine the asphalt material samples that meet the user's query conditions based on the second-level screening index. For example, if a user wants to query asphalt material samples with a dynamic modulus greater than 3000MPa at 20°C and 10Hz, the three-level screening index can be used to screen according to the dynamic modulus and finally determine the asphalt material samples that meet the conditions. To build a multidimensional index structure, it is necessary to establish the association between different indexes. For example, the preparation parameters, microscopic characteristic parameters, and macroscopic mechanical properties data tables are associated through foreign keys to achieve a multidimensional index structure.

[0055] Step S24: creating an asphalt material database management system for the asphalt material sample based on the asphalt multidimensional index structure.

[0056] In an embodiment of the present invention, a suitable relational database management system (RDBMS) is selected, such as MySQL, PostgreSQL, or others. In the relational database management system, the table structure of the database is designed, and multiple asphalt material data tables are created to store information such as preparation parameter data, microscopic characteristic parameter data, macroscopic mechanical property data, image data path, etc. The data tables are associated with each other through sample numbers, and a data import program is written to import various data of asphalt materials into the database. The data import program needs to ensure the correctness and consistency of the data. A user-friendly graphical interface (GUI) or web interface is developed to allow users to query the database by entering query conditions (such as preparation parameters, microscopic characteristic parameter range, macroscopic mechanical property range). Various query functions are provided, such as querying according to preparation parameters, querying according to microscopic characteristic parameters, querying according to macroscopic mechanical properties, and multi-condition combination query. The query results need to be displayed in the form of tables, charts, etc. The query function needs to be based on a multidimensional index structure to achieve fast query response. The database management system is tested and optimized to ensure its performance and stability, and finally the database management system is created.

[0057] Preferably, the multidimensional index structure construction specifically includes the following steps: Constructing a B+ tree index for the preparation parameter data table, with gradation type, modifier type and aging condition as joint keys; An R-tree index is constructed for the microscopic characteristic parameters of the asphalt material, with porosity, aggregate size distribution and asphalt film thickness as spatial dimensions; Constructing a hash index for the macroscopic mechanical property data of the asphalt material, with dynamic modulus, creep stiffness and fatigue life as hash keys; The three-level indexes are integrated into a logical association table through a database view.

[0058] In an embodiment of the present invention, fields that need to be indexed in the preparation parameter data table are selected, namely, gradation type, modifier type, and aging condition. These three fields together constitute a joint key, which can uniquely identify an asphalt material sample. Then, based on the joint key, a B+ tree index is constructed on the preparation parameter data table. Fields that need to be indexed in the microscopic characteristic parameters of the asphalt material are selected, namely, porosity, aggregate size distribution, and asphalt film thickness. Since these parameters all have numerical attributes, they can be regarded as coordinates in a multidimensional space. Then, an R-tree index is constructed on the microscopic characteristic parameter data based on porosity, aggregate size distribution, and asphalt film thickness. Fields that need to be indexed in the macroscopic mechanical properties data are selected, namely, dynamic modulus, creep stiffness, and fatigue life. These fields can be used as hash keys. A hash index is constructed on the macroscopic mechanical properties data based on dynamic modulus, creep stiffness, and fatigue life. Three basic tables are created. These three basic tables correspond to the preparation parameter data table, the microscopic characteristic parameter data table, and the macroscopic mechanical properties data table, respectively. A database view is created. The database view organizes the data in the basic table according to a certain logic to form a logically related table. The database view integrates the three indexes into a logically related table. The logically related table can integrate the data in different tables according to the user's needs, simplify the user's query operation, and improve the query efficiency.

[0059] The present application is to image and scan asphalt material samples with different gradations, different modifier types and different aging states, obtain multi-scale microscopic image data including surface morphology, roughness and three-dimensional structure, and accurately extract microscopic characteristic parameters of asphalt materials, such as porosity, pore size distribution, aggregate shape, aggregate size distribution, asphalt film thickness and interface bonding state. By combining microscopic characteristic parameters with macroscopic performance data, the constructed asphalt material database management system supports multi-dimensional queries based on preparation parameters, microscopic characteristic parameters and macroscopic performance data. Users can flexibly set preparation conditions (such as gradation type, modifier type, aging conditions), microstructural characteristics (such as porosity, roughness) and macroscopic performance indicators (such as dynamic modulus, fatigue life) according to actual needs, and quickly find asphalt materials that meet specific requirements. This multi-dimensional design greatly improves the flexibility of data management and query efficiency.

[0060] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0061] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for establishing an asphalt material database management system, characterized in that: The following steps are involved: Using a scanning electron microscope, an atomic force microscope and an X-ray tomography device, imaging and scanning are performed on asphalt material samples of different gradations, different modifier types and different aging states, respectively, to obtain multi-scale asphalt material microscopic image data; wherein the multi-scale asphalt material microscopic image data includes an asphalt material surface morphology image, an asphalt material surface roughness image and an asphalt material three-dimensional structure image; Performing denoising, contrast enhancement and geometric distortion correction preprocessing operations on the multi-scale asphalt material microscopic image data; Analyzing the microscopic characteristics of the material sample according to the pre-processed multi-scale asphalt material microscopic image data to obtain the microscopic characteristic parameters of the asphalt material; An asphalt material database management system is created for the microscopic characteristic parameters of the asphalt material based on a relational database management system; wherein the asphalt material database management system supports multi-dimensional queries based on preparation parameters, microscopic characteristic parameters and macroscopic performance data, and outputs asphalt material data that meets the conditions.

2. The method for establishing an asphalt material database management system according to claim 1, characterized in that: The imaging scan comprises the following steps: According to the preset gradation range, modifier type and aging conditions, asphalt material samples with different gradations, different modifier types and different aging conditions are prepared, and the asphalt material samples are subjected to pretreatment operations such as cutting and polishing to obtain sample surfaces that meet the test requirements; Fixing the pretreated asphalt material sample on a sample stage of a scanning electron microscope, an atomic force microscope and an X-ray tomography device, and determining at least 5 different imaging areas according to the sample size and the target magnification; Start a scanning electron microscope, an atomic force microscope and an X-ray tomography device, scan the imaging areas one by one, and save the scanning image data of each area, to obtain a surface morphology image of the asphalt material, a surface roughness image of the asphalt material and a three-dimensional structure image of the asphalt material respectively; wherein, the surface morphology image of the asphalt material is obtained by a scanning electron microscope; the surface roughness image of the asphalt material is obtained by an atomic force microscope; and the three-dimensional structure image of the asphalt material is obtained by an X-ray tomography device.

3. The method for establishing an asphalt material database management system according to claim 1, characterized in that: The material sample microscopic characteristic analysis comprises the following steps: Identifying pore areas according to the three-dimensional structural image of the asphalt material, and performing pore characteristic analysis to obtain pore characteristic data; Based on the surface morphology image of the asphalt material and the three-dimensional structure image of the asphalt material, the aggregate particle contour is identified, and the aggregate contour feature analysis is performed to obtain aggregate feature data; The asphalt film is segmented according to the asphalt material surface morphology image, and the asphalt interface state is analyzed through the asphalt material surface roughness image to obtain the asphalt film characteristic data; The pore characteristic data, aggregate characteristic data and asphalt film characteristic data are quantified, and the relationship between the characteristic parameters is established to obtain the microscopic characteristic parameters of the asphalt material.

4. The method for establishing an asphalt material database management system according to claim 3, characterized in that: The pore characteristic data includes asphalt material porosity data, pore size distribution data and pore size connectivity data, and the pore characteristic analysis includes the following steps: Grayscale processing is performed on the three-dimensional structural image of the asphalt material to convert the color image into a grayscale image; The segmentation threshold interval is set to [T1, T2] based on the grayscale image, pixels with grayscale values ​​lower than T1 in the image are identified as pore areas, and pixels with grayscale values ​​higher than T2 are identified as non-pore areas, and the image is converted into a binary image to obtain asphalt pore binary image data; wherein T1 is set to 20% of the average grayscale value of the three-dimensional structure image of the asphalt material, and T2 is set to 80% of the average grayscale value of the three-dimensional structure image of the asphalt material; for pixels with grayscale values ​​between T1 and T2, secondary attribution discrimination is performed according to the grayscale values ​​of their neighboring pixels; Based on the asphalt pore binary image data, the volume corresponding to each pore pixel is calculated using the pixel size and the scanning layer thickness in the three-dimensional structure image of the asphalt material, and the volumes of all pore pixels are accumulated to obtain the total pore volume data of the sample; Extracting the total volume of the asphalt sample according to the three-dimensional structural image of the asphalt material, and dividing the total pore volume data of the sample by the total volume of the asphalt sample to obtain the porosity data of the asphalt material; Calculating the distance from each pore pixel to the nearest non-pore pixel according to the asphalt pore binary image data, thereby obtaining pore size distribution data; Extracting the central skeleton line of the pore area based on the asphalt pore binary image data, and defining the connected skeleton line segments as a connected pore cluster; The number of connected paths and the average path length of the connected pore clusters are counted to obtain data characterizing pore size connectivity.

5. The method for establishing an asphalt material database management system according to claim 3, characterized in that: The aggregate feature data includes aggregate shape data, aggregate direction data and aggregate size distribution data, and the aggregate profile feature analysis includes the following steps: The Canny edge detection algorithm is used to detect the edge of aggregate particles in the surface morphology image of the asphalt material to obtain the edge data of asphalt aggregate particles; wherein the standard deviation σ of the Gaussian filter is set to 1.5, the high threshold is 80% of the maximum gray value of the three-dimensional structure image of the asphalt material, and the low threshold is 40% of the maximum gray value of the three-dimensional structure image of the asphalt material; Tracking the closed contour of aggregate particles based on the edge data of the asphalt aggregate particles; Performing morphological analysis based on the closed contours of the aggregate particles and determining the shapes of the aggregate particles to obtain aggregate shape data; Fitting the minimum circumscribed rectangle of each aggregate particle according to the closed contour line of the aggregate particle, calculating the angle between the major axis direction of the rectangle and the preset reference coordinate system, and obtaining aggregate direction data; The area and equivalent diameter of each aggregate particle are calculated according to the closed contour line of the aggregate particle to obtain aggregate size distribution data.

6. The method for establishing an asphalt material database management system according to claim 3, characterized in that: The asphalt film characteristic data includes asphalt film thickness, asphalt film uniformity evaluation data and asphalt film-aggregate interface bonding state data, and the asphalt interface state analysis includes the following steps: Segment the asphalt film and the aggregate region according to the asphalt material surface morphology image and the asphalt material three-dimensional structure image to obtain the asphalt film region and the aggregate region respectively; Performing a three-phase contact point analysis on the asphalt film area and the aggregate area to obtain contact characteristic data of the asphalt sample; The interface transition region distance is set according to the contact characteristic data of the asphalt sample to determine the interface transition region between the aggregate and the asphalt film; Taking the aggregate particle closed contour line as a reference line, detecting the vertical distance from the reference line to the interface transition area as the asphalt film thickness at the position; Calculating the film thickness average, standard deviation and coefficient of variation according to the asphalt film thickness, and evaluating the uniformity of the asphalt film to obtain asphalt film uniformity evaluation data; The roughness state of the interface region is analyzed based on the surface roughness image of the asphalt material to obtain the asphalt film-aggregate interface bonding state data.

7. The method for establishing an asphalt material database management system according to claim 6, characterized in that: The interface region roughness analysis comprises the following steps: Performing image mapping between the interface transition region and the surface roughness image of the asphalt material; Divide the roughness value of the interface transition area by the roughness value of the aggregate area, and then divide the difference by the roughness value of the aggregate area to obtain the roughness change rate of the interface area; Set the roughness threshold and asphalt film thickness threshold; If the roughness change rate of the interface area is greater than the roughness threshold, and the asphalt film thickness is greater than the asphalt film thickness threshold, it is determined that the interface bonding state between the asphalt film and the aggregate is good, otherwise it is determined that the interface bonding state is poor, so as to obtain the asphalt film-aggregate interface bonding state data.

8. The method for establishing an asphalt material database management system according to claim 6, characterized in that: The three-phase contact point analysis includes the following steps: Superimposing the edges of the asphalt film area and the aggregate area, and comparing the edge coordinate information, screening out pixel pairs whose distance is less than a preset contact threshold, and using these pixel pairs as candidate contact points; Calculating the grayscale value change rate of pixels within a neighborhood radius of the candidate contact point, and if the grayscale value changes in at least three directions within the neighborhood of the candidate contact point are greater than a preset grayscale value change threshold, determining that the candidate contact point is a three-phase contact point; Fitting the tangent line of the asphalt film at the contact point with the three-phase contact point as the center to obtain the asphalt film tangent line fitting data; Calculating the normal direction of the aggregate surface at the three-phase contact point, and calculating the contact angle according to the asphalt film tangent fitting data to obtain the asphalt film contact angle; The effective angle is screened according to the contact angle of the asphalt film, and the average contact angle is calculated to obtain the contact characteristic data of the asphalt sample.

9. The method for establishing an asphalt material database management system according to claim 1, characterized in that: The asphalt material database management system creation includes the following steps: Defining preparation parameter fields for the asphalt material sample, and creating a preparation parameter data table in a relational database; wherein the defined preparation parameter fields include gradation type, modifier type, aging condition, and setting a composite primary key; the composite primary key is composed of gradation type code, modifier type code and aging condition code; Obtaining macroscopic mechanical performance data of asphalt materials; wherein the macroscopic mechanical performance data includes dynamic modulus, creep stiffness and fatigue life data; A multidimensional index structure of asphalt is constructed according to the preparation parameter data table, the macroscopic mechanical property data of asphalt materials and the microscopic characteristic parameters of asphalt materials, wherein the preparation parameters in the preparation parameter data table are used as the primary screening index, the microscopic characteristic parameters in the microscopic characteristic parameters of asphalt materials are used as the secondary screening index, and the macroscopic mechanical properties in the macroscopic mechanical property data of asphalt materials are used as the tertiary screening index; An asphalt material database management system is established for the asphalt material samples based on the asphalt multidimensional index structure.

10. The method for establishing an asphalt material database management system according to claim 9, characterized in that: The multidimensional index structure construction comprises the following steps: Constructing a B+ tree index for the preparation parameter data table, with gradation type, modifier type and aging condition as joint keys; An R-tree index is constructed for the microscopic characteristic parameters of the asphalt material, with porosity, aggregate size distribution and asphalt film thickness as spatial dimensions; Constructing a hash index for the macroscopic mechanical property data of the asphalt material, with dynamic modulus, creep stiffness and fatigue life as hash keys; The joint key, the spatial dimension and the hash key are integrated as a three-level index into a logical association table through a database view to obtain an asphalt multidimensional index structure.

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