Asphalt road engineering construction quality real-time monitoring method

Abstract

The invention relates to the technical field of engineering construction quality monitoring, and particularly discloses an asphalt road engineering construction quality real-time monitoring method. Comprising the following steps of S1, asphalt road image information collection, S2, asphalt road image feature extraction, S3, asphalt road flatness abnormity monitoring analysis, S4, asphalt road compactness abnormity monitoring analysis, S5, asphalt road construction quality monitoring analysis and S6, asphalt road construction quality abnormity early warning. According to the method, the image balance degree feature information and the image compactness feature information are extracted, the pavement flatness balance coefficient and the pavement compactness evaluation coefficient are obtained through analysis, then the construction quality evaluation index is obtained through analysis, the construction quality is comprehensively evaluated, the monitoring result is displayed in real time, and early warning is given out when abnormity occurs. By introducing an image processing technology, real-time monitoring and intelligent evaluation of construction quality are realized, monitoring efficiency and accuracy are improved, and powerful support is provided for construction and management of road engineering.

Description

Technical Field

[0001] The invention relates to the technical field of engineering construction quality monitoring, and in particular to a method for real-time monitoring of asphalt road engineering construction quality. Background Art

[0002] As an important part of the modern transportation network, the construction quality of asphalt roads is directly related to the service life, driving safety and maintenance costs of roads. With the acceleration of urbanization and the continuous increase in traffic flow, the requirements for the construction quality of asphalt roads are getting higher and higher. At the same time, the rapid development of science and technology has brought new opportunities for the quality monitoring of asphalt road construction. Image processing technology can quickly and accurately acquire and analyze a large amount of image information, providing strong technical support for real-time monitoring of asphalt road construction quality. Through high-resolution image acquisition equipment, characteristic information in the road construction process can be obtained, providing a rich data basis for subsequent quality evaluation.

[0003] The traditional real-time monitoring method of asphalt road construction quality mainly relies on manual inspection and sampling inspection. In terms of manual inspection, professionals conduct on-site inspections and use observation, touch and other means to evaluate the laying quality of asphalt mixtures; while sampling inspections are to regularly extract asphalt mixture samples and send them to the laboratory for physical and chemical performance tests. In addition, raw materials such as asphalt and aggregates are also sent to the laboratory for performance testing by sampling. During the construction process, tools such as levels and theodolites are also used to measure geometric parameters such as the flatness of the road surface. However, although these methods can reflect the construction quality to a certain extent, they still have many limitations.

[0004] The limitations of the traditional real-time monitoring method of asphalt road construction quality: First, the sampling inspection method is not only inefficient, but also difficult to fully and real-time reflect the real situation of construction quality. Secondly, the quality judgment of manual inspection depends on the experience and subjective judgment of the inspectors, and lacks objective and unified standards, which may lead to different evaluations of the same construction section by different inspectors, affecting the accuracy of quality control. In addition, the traditional method lacks convenient data recording means, making it difficult to trace and analyze the construction quality, which is not conducive to the continuous improvement and optimization of construction quality. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a method for real-time monitoring of the construction quality of an asphalt road project to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a method for real-time monitoring of the construction quality of an asphalt road project, comprising the following steps: S1: asphalt road image information collection, S2: asphalt road image feature extraction, S3: asphalt road flatness abnormality monitoring and analysis, S4: asphalt road compaction abnormality monitoring and analysis, S5: asphalt road construction quality monitoring and analysis and S6: asphalt road construction quality abnormality warning.

[0007] S1: Asphalt road image information collection: Install a high-definition camera to shoot the target area during the asphalt road construction process, obtain the asphalt monitoring road surface image, and mark it as the first monitoring road surface image; S2: Extracting features of asphalt road images: performing a preprocessing operation on the first monitored road image to obtain a second monitored road image, and extracting feature information of the asphalt road image based on the second monitored road image, wherein the feature information includes image balance feature information and image density feature information; S3: Asphalt road flatness abnormality monitoring and analysis: Based on the image balance feature information, the asphalt road flatness balance is analyzed to obtain the road surface flatness balance coefficient of the asphalt road, and whether it is abnormal is determined according to the road surface flatness balance coefficient, and an early warning message is issued for data with abnormal judgment results; S4: Monitoring and analysis of abnormal compactness of asphalt roads: Based on the image density feature information, the compactness of asphalt roads is analyzed to obtain the pavement compactness evaluation coefficient of asphalt roads, and the pavement compactness evaluation coefficient is used to determine whether it is abnormal, and an early warning message is issued for data with abnormal judgment results; S5: Asphalt road construction quality monitoring and analysis: Based on the road surface flatness balance coefficient and road surface compaction evaluation coefficient, the asphalt road construction quality is analyzed to obtain the asphalt road construction quality evaluation index, and the quality is evaluated to output the asphalt road construction quality monitoring results; S6: Asphalt road construction quality abnormality warning: The asphalt road construction quality monitoring results are displayed in real time on the visual interface of the monitoring center, and alarm information is issued for abnormal data.

[0008] Preferably, the specific implementation method of the asphalt road image information collection is as follows: The asphalt construction road is determined as the target monitoring area, the target monitoring area is photographed, and an asphalt monitoring road surface image is obtained, and the asphalt monitoring road surface image is divided into n monitoring sub-areas, i=1, 2, 3, ..., n, i is the number of each monitoring sub-area.

[0009] Preferably, the specific implementation method of performing the preprocessing operation on the first monitored road surface image is as follows: Based on the acquired first monitored road surface image, the first monitored road surface image is preprocessed by a grayscale processing unit, a filtering and denoising processing unit, and a histogram equalization processing unit to obtain a second monitored road surface image.

[0010] Preferably, the image balance characteristic information includes the image particle circularity factor and the image particle area discreteness. The specific extraction execution method of the image balance characteristic information is as follows: In the first step, the particles are separated from the background of the second monitored road image using image segmentation technology, and each particle is marked to obtain the image particle marking points, and the image particle area of ​​each monitored sub-area is calculated. Pa i , the calculation model is as follows: ,in, Pa i represents the image particle area of ​​the i-th monitoring sub-region, M Indicates the height of the image. N Indicates the width of the image. Indicates that the image of the i-th monitoring sub-area is at position The pixel value at ; The second monitored road surface image is introduced into the edge detection algorithm to identify the edge line of each particle in the second monitored road surface image, and the length of the curve is calculated as the perimeter of the edge line of the image particle by fitting a curve to the edge line of the image particle. Lp ; Read the image particle area of ​​the i-th monitoring sub-area Pa i and the perimeter of the edge line of the image particles in the i-th monitoring sub-area Lp i , calculate the circularity factor of the image particles in the i-th monitoring sub-area Prf i , the calculation model is as follows: ,in, represents pi; In the second step, the area of ​​each image particle is marked as , m represents the total number of image particles, j=1, 2, 3, ..., m, j is the number of each image particle; Calculate the discrete degree of image particle area, the calculation model is as follows: ,in, Pdd i Indicates the discrete degree of the image particle area in the i-th monitoring sub-area, Pa ij Represents the area of ​​the jth image particle in the ith monitoring sub-region.

[0011] Preferably, the image density feature information includes the image road surface crack rate and the image road surface porosity. The specific extraction execution method of the image density feature information is as follows: The first step is to import the second monitored road surface image into the edge detection algorithm to identify the crack edge line in the second monitored road surface image, and use the threshold segmentation technology to convert the second monitored road surface image into a binary image, with the crack area being white and the background being black, and count the number of pixels in the crack area of ​​each monitored sub-area Nc i and the number of pixels in the monitored road image area of ​​each monitored sub-area Nt i , substituting it into the formula Get the pavement image crack rate of the i-th monitoring sub-area Cr i ; In the second step, the second monitored road surface image is divided into pore and non-pore areas by binarization processing, and the number of pixels in the pore area of ​​each monitored sub-area is counted. Np i and the number of pixels in the monitored road image area of ​​each monitored sub-area Nt i , substituting it into the formula Get the porosity of the road surface image in the i-th monitoring sub-area Pr i .

[0012] Preferably, the specific implementation method of the asphalt road leveling abnormality monitoring and analysis is as follows: S31: Reading the image particle circularity factor of the i-th monitoring sub-area in the image balance feature information Prf i , substituting it into the formula Get the image particle circularity deviation factor of the i-th monitoring sub-area Rdr i ,in, Prf 0 represents the preset standard image particle circularity factor; S32: Reading the image particle area discreteness of the i-th monitoring sub-area in the image balance feature information Pdd i , substituting it into the formula Get the coefficient of variation of the image particle area of ​​the i-th monitoring sub-area Cov i ,in, represents the average value of the particle area of ​​the image in the i-th monitoring sub-region; S33: Calculate the pavement smoothness balance coefficient of asphalt roads FEC , the calculation model is as follows: ,in, Rdr 0 means the preset maximum value of the circularity deviation factor of the image particles. e represents a natural constant, n represents the total number of each monitoring sub-area, and i represents the number of each monitoring sub-area; S34: Extract the pavement smoothness balance coefficient of the asphalt road and compare it with the preset pavement smoothness balance coefficient threshold to evaluate the pavement smoothness balance of the asphalt road. If the pavement smoothness balance coefficient is greater than or equal to the preset pavement smoothness balance coefficient threshold, it is determined that there is no abnormality in the pavement smoothness of the asphalt road. If the pavement smoothness balance coefficient is less than the preset pavement smoothness balance coefficient threshold, it is determined that there is an abnormality in the pavement smoothness of the asphalt road, and a warning message is issued for data with abnormal judgment results.

[0013] Preferably, the specific implementation method of the asphalt road compaction abnormality monitoring and analysis is as follows: S41: Reading the pavement image crack rate of the ith monitoring sub-area in the image density feature information Cr i , substituting it into the formula Get the image crack deviation of the i-th monitoring sub-area Cdd i ,in, Cr 0 indicates the preset maximum crack rate of the pavement image; S42: Calculate the pavement compactness evaluation coefficient of asphalt roads DEC , the calculation model is as follows: ,in, Pr i represents the porosity of the road surface image in the ith monitoring sub-area, e represents a natural constant, n represents the total number of each monitoring sub-area, and i represents the number of each monitoring sub-area; S43: Extract the pavement density evaluation coefficient of the asphalt road and compare it with the preset pavement density evaluation coefficient threshold to evaluate the pavement density of the asphalt road. If the pavement density evaluation coefficient is greater than or equal to the preset pavement density evaluation coefficient threshold, it is determined that there is no abnormality in the pavement density of the asphalt road. If the pavement density evaluation coefficient is less than the preset pavement density evaluation coefficient threshold, it is determined that there is an abnormality in the pavement density of the asphalt road, and a warning message is issued for data with abnormal judgment results.

[0014] Preferably, the specific implementation method of the asphalt road construction quality monitoring and analysis is as follows: S51: Extract the pavement smoothness balance coefficient of asphalt roads FEC and pavement compactness evaluation coefficient of asphalt roads DEC , monitor the construction quality of asphalt road projects, and analyze the construction quality evaluation index of asphalt roadsQEI , the calculation model is as follows: ; S52: Assess the construction quality of the asphalt road project, extract the construction quality evaluation index of the asphalt road and compare it with the preset construction quality evaluation index threshold. If the construction quality evaluation index of the asphalt road is greater than or equal to the preset construction quality evaluation index threshold, the construction quality of the asphalt road project is judged to be qualified. If the construction quality evaluation index of the asphalt road is less than the preset construction quality evaluation index threshold, the construction quality of the asphalt road project is judged to be unqualified. Issue a warning message for the data judged to be unqualified, and output the asphalt road project construction quality monitoring results.

[0015] Technical effects and advantages of the present invention: 1. The present invention collects real-time image information of the asphalt road construction process by installing a high-definition camera, which can quickly capture and process key information in the construction process, timely discover and warn potential flatness and density anomalies, significantly improve the monitoring efficiency and response speed of construction quality, and use image processing technology to automatically extract feature information of road surface images, replacing traditional manual detection and sampling inspection methods, reducing errors caused by human factors, and improving the accuracy and objectivity of monitoring; 2. Based on the feature information of the extracted road surface image, the present invention analyzes and obtains the road surface flatness balance coefficient and the road surface compactness evaluation coefficient, comprehensively evaluates the construction quality of asphalt road projects, and displays the asphalt road project construction quality monitoring results in real time on the visual interface of the monitoring center, so that management personnel can intuitively and clearly understand the construction quality situation, facilitate rapid decision-making and adjustment, and improve management efficiency and convenience; 3. Through real-time monitoring and early warning mechanisms, the present invention can timely discover quality problems before they occur and take measures to intervene, effectively preventing rework and increased repair costs caused by construction quality problems, and improving the economic and social benefits of the project. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0017] Figure 1 The present invention is a flowchart of a method for real-time monitoring of asphalt road construction quality.

[0018] Figure 2 This is an asphalt road image feature extraction information diagram of a real-time monitoring method for asphalt road engineering construction quality of the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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 ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] See also Figure 1 As shown, the present invention provides a real-time monitoring method for the construction quality of asphalt road projects, comprising the following steps: S1: asphalt road image information collection, S2: asphalt road image feature extraction, S3: asphalt road flatness abnormality monitoring and analysis, S4: asphalt road compaction abnormality monitoring and analysis, S5: asphalt road construction quality monitoring and analysis and S6: asphalt road construction quality abnormality warning.

[0021] S1: Asphalt road image information collection: Install a high-definition camera to shoot the target area during the asphalt road construction process, obtain the asphalt monitoring road surface image, and mark it as the first monitoring road surface image; In this embodiment, it should be specifically explained that the specific execution method of collecting the asphalt road image information is as follows: The asphalt construction road is determined as the target monitoring area, the target monitoring area is photographed, an asphalt monitoring road surface image is obtained, and the asphalt monitoring road surface image is divided into n monitoring sub-areas, i=1, 2, 3, ..., n, i is the number of each monitoring sub-area; A high-definition camera is installed on the construction equipment at the asphalt road construction site to take pictures of the asphalt road construction site at a frequency of once every three seconds. The distance between the installed camera and the ground is controlled within five meters to obtain the asphalt monitoring road surface image.

[0022] S2: Extracting features of asphalt road images: performing a preprocessing operation on the first monitored road image to obtain a second monitored road image, and extracting feature information of the asphalt road image based on the second monitored road image, wherein the feature information includes image balance feature information and image density feature information; See also Figure 2 As shown, the asphalt road image feature extraction includes image balance feature information and image density feature information; In this embodiment, it should be specifically explained that the specific execution method of the preprocessing operation on the first monitored road surface image is as follows: Based on the acquired first monitored road surface image, preprocessing the first monitored road surface image by a grayscale processing unit, a filtering and denoising processing unit, and a histogram equalization processing unit to obtain a second monitored road surface image; The grayscale processing unit converts the RGB color image of the first monitored road surface image into a grayscale image by a weighted average method; The filtering and denoising processing unit applies a Gaussian filtering algorithm to reduce or eliminate noise in the grayscale image, thereby improving the clarity and quality of the image; The histogram equalization processing unit adjusts the grayscale distribution of the grayscale image of the first monitored road surface image to make it more uniform, thereby improving the contrast and brightness of the image.

[0023] In this embodiment, it should be specifically explained that the specific content of the asphalt road image feature extraction is as follows: Extracting feature information of the asphalt road image based on the second monitored road surface image, wherein the feature information includes image balance feature information and image density feature information; The image balance characteristic information includes the image particle circularity factor and the image particle area discreteness; The specific extraction execution method of the image balance feature information is as follows: In the first step, the particles are separated from the background of the second monitored road image using image segmentation technology (such as the connected region labeling algorithm), and each particle is marked to obtain the image particle marking points, and the image particle area of ​​each monitored sub-area is calculated. Pa i , the calculation model is as follows: ,in, Pa i represents the image particle area of ​​the i-th monitoring sub-region, M Indicates the height of the image. N Indicates the width of the image. Indicates that the image of the i-th monitoring sub-area is at position The pixel value at ; In this embodiment, it should be specifically explained that the number of pixels marked as particles is counted, and each pixel represents a unit area, so the total number of pixels is equal to the area of ​​the image particles.

[0024] The second monitored road surface image is introduced into an edge detection algorithm (such as Canny edge detection), the edge line of each particle in the second monitored road surface image is identified, and a curve is fitted to the edge line of the image particle, and the length of the curve is calculated as the perimeter of the edge line of the image particle. Lp ; Read the image particle area of ​​the i-th monitoring sub-area Pa i and the perimeter of the edge line of the image particles in the i-th monitoring sub-area Lp i , calculate the circularity factor of the image particles in the i-th monitoring sub-area Prfi , the calculation model is as follows: ,in, represents pi; In the second step, the area of ​​each image particle is marked as , m represents the total number of image particles, j=1, 2, 3, ..., m, j is the number of each image particle; Calculate the discrete degree of image particle area, the calculation model is as follows: ,in, Pdd i Indicates the discrete degree of the image particle area in the i-th monitoring sub-area, Pa ij represents the area of ​​the jth image particle in the ith monitoring sub-region; In this embodiment, it should be specifically explained that the image density feature information includes the image road surface crack rate and the image road surface porosity rate; The specific extraction execution method of the image density feature information is as follows: The first step is to import the second monitored road surface image into an edge detection algorithm (such as Canny edge detection) to identify the crack edge line in the second monitored road surface image, and use threshold segmentation technology (such as Otsu's method, local threshold method) to convert the second monitored road surface image into a binary image, with the crack area being white and the background being black, and count the number of pixels in the crack area of ​​each monitored sub-area Nc i and the number of pixels in the monitored road image area of ​​each monitored sub-area Nt i , substituting it into the formula Get the pavement image crack rate of the i-th monitoring sub-area Cr i ; In the second step, the second monitored road surface image is divided into pore and non-pore areas by binarization processing, and the number of pixels in the pore area of ​​each monitored sub-area is counted. Np i and the number of pixels in the monitored road image area of ​​each monitored sub-area Nt i , substituting it into the formula Get the porosity of the road surface image in the i-th monitoring sub-area Pr i .

[0025] S3: Asphalt road flatness abnormality monitoring and analysis: Based on the image balance feature information, the asphalt road flatness balance is analyzed to obtain the road surface flatness balance coefficient of the asphalt road, and whether it is abnormal is determined according to the road surface flatness balance coefficient, and an early warning message is issued for data with abnormal judgment results; In this embodiment, it should be specifically explained that the specific execution method of the asphalt road flatness abnormality monitoring and analysis is as follows: S31: Reading the image particle circularity factor of the i-th monitoring sub-area in the image balance feature information Prf i , substituting it into the formula Get the image particle circularity deviation factor of the i-th monitoring sub-area Rdr i ,in, Prf 0 represents the preset standard image particle circularity factor; S32: Reading the image particle area discreteness of the i-th monitoring sub-area in the image balance feature information Pdd i , substituting it into the formula Get the coefficient of variation of the image particle area of ​​the i-th monitoring sub-area Cov i ,in, represents the average value of the particle area of ​​the image in the i-th monitoring sub-region; S33: Calculate the pavement smoothness balance coefficient of asphalt roads FEC , the calculation model is as follows: ,in, Rdr 0 means the preset maximum value of the circularity deviation factor of the image particles. e represents a natural constant, n represents the total number of each monitoring sub-area, and i represents the number of each monitoring sub-area; S34: Extract the pavement smoothness balance coefficient of the asphalt road and compare it with the preset pavement smoothness balance coefficient threshold to evaluate the pavement smoothness balance of the asphalt road. If the pavement smoothness balance coefficient is greater than or equal to the preset pavement smoothness balance coefficient threshold, it is determined that there is no abnormality in the pavement smoothness of the asphalt road. If the pavement smoothness balance coefficient is less than the preset pavement smoothness balance coefficient threshold, it is determined that there is an abnormality in the pavement smoothness of the asphalt road, and a warning message is issued for data with abnormal judgment results.

[0026] S4: Monitoring and analysis of abnormal compactness of asphalt roads: Based on the image density feature information, the compactness of asphalt roads is analyzed to obtain the pavement compactness evaluation coefficient of asphalt roads, and the pavement compactness evaluation coefficient is used to determine whether it is abnormal, and an early warning message is issued for data with abnormal judgment results; In this embodiment, it should be specifically explained that the specific execution method of the asphalt road compaction abnormality monitoring and analysis is as follows: S41: Reading the pavement image crack rate of the ith monitoring sub-area in the image density feature information Cr i , substituting it into the formula Get the image crack deviation of the i-th monitoring sub-area Cdd i ,in, Cr 0 indicates the preset maximum crack rate of the pavement image; S42: Calculate the pavement compactness evaluation coefficient of asphalt roads DEC , the calculation model is as follows: ,in, Pr i represents the porosity of the road surface image in the ith monitoring sub-area, e represents a natural constant, n represents the total number of each monitoring sub-area, and i represents the number of each monitoring sub-area; S43: Extract the pavement density evaluation coefficient of the asphalt road and compare it with the preset pavement density evaluation coefficient threshold to evaluate the pavement density of the asphalt road. If the pavement density evaluation coefficient is greater than or equal to the preset pavement density evaluation coefficient threshold, it is determined that there is no abnormality in the pavement density of the asphalt road. If the pavement density evaluation coefficient is less than the preset pavement density evaluation coefficient threshold, it is determined that there is an abnormality in the pavement density of the asphalt road, and a warning message is issued for data with abnormal judgment results.

[0027] S5: Asphalt road construction quality monitoring and analysis: Based on the road surface flatness balance coefficient and road surface compaction evaluation coefficient, the asphalt road construction quality is analyzed to obtain the asphalt road construction quality evaluation index, and the quality is evaluated to output the asphalt road construction quality monitoring results; In this embodiment, it should be specifically explained that the specific execution method of the asphalt road construction quality monitoring and analysis is as follows: S51: Extract the pavement smoothness balance coefficient of asphalt roads FEC and pavement compactness evaluation coefficient of asphalt roads DEC , monitor the construction quality of asphalt road projects, and analyze the construction quality evaluation index of asphalt roads QEI , the calculation model is as follows: ; S52: Assess the construction quality of the asphalt road project, extract the construction quality evaluation index of the asphalt road and compare it with the preset construction quality evaluation index threshold. If the construction quality evaluation index of the asphalt road is greater than or equal to the preset construction quality evaluation index threshold, the construction quality of the asphalt road project is judged to be qualified. If the construction quality evaluation index of the asphalt road is less than the preset construction quality evaluation index threshold, the construction quality of the asphalt road project is judged to be unqualified. Issue a warning message for the data judged to be unqualified, and output the asphalt road project construction quality monitoring results.

[0028] S6: Asphalt road construction quality abnormality warning: The asphalt road construction quality monitoring results are displayed in real time on the visual interface of the monitoring center, and alarm information is issued for abnormal data.

[0029] In this embodiment, it should be specifically explained that the specific implementation method of the abnormal early warning of asphalt road construction quality is as follows: The asphalt road construction quality monitoring results are displayed in real time on the visual interface of the monitoring center, so that construction management personnel can intuitively see the construction quality status, which is convenient for timely discovering problems and taking measures; construction management personnel can promptly direct on-site construction personnel to adjust the construction process to ensure construction quality based on the alarm information.

[0030] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

[0031] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A real-time monitoring method for asphalt road construction quality, characterized in that: The following steps are involved: S1: Asphalt road image information collection: Install a high-definition camera to shoot the target area during the asphalt road construction process, obtain the asphalt monitoring road surface image, and mark it as the first monitoring road surface image; S2: Extracting features of asphalt road images: performing a preprocessing operation on the first monitored road image to obtain a second monitored road image, and extracting feature information of the asphalt road image based on the second monitored road image, wherein the feature information includes image balance feature information and image density feature information; S3: Asphalt road flatness abnormality monitoring and analysis: Based on the image balance feature information, the asphalt road flatness balance is analyzed to obtain the road surface flatness balance coefficient of the asphalt road, and whether it is abnormal is determined according to the road surface flatness balance coefficient, and an early warning message is issued for data with abnormal judgment results; S4: Monitoring and analysis of abnormal compactness of asphalt roads: Based on the image density feature information, the compactness of asphalt roads is analyzed to obtain the pavement compactness evaluation coefficient of asphalt roads, and the pavement compactness evaluation coefficient is used to determine whether it is abnormal, and a warning message is issued for data with abnormal judgment results; S5: Asphalt road construction quality monitoring and analysis: Based on the road surface flatness balance coefficient and road surface compaction evaluation coefficient, the asphalt road construction quality is analyzed to obtain the asphalt road construction quality evaluation index, and the quality is evaluated to output the asphalt road construction quality monitoring results; S6: Asphalt road construction quality abnormality warning: The asphalt road construction quality monitoring results are displayed in real time on the visual interface of the monitoring center, and alarm information is issued for abnormal data.

2. A method for real-time monitoring of asphalt road construction quality according to claim 1, characterized in that: The specific implementation method of the asphalt road image information collection is as follows: The asphalt construction road is determined as the target monitoring area, the target monitoring area is photographed, and an asphalt monitoring road surface image is obtained. The asphalt monitoring road surface image is divided into n monitoring sub-areas, i=1, 2, 3, ..., n, i is the number of each monitoring sub-area.

3. A method for real-time monitoring of asphalt road construction quality according to claim 1, characterized in that: The specific implementation method of performing the preprocessing operation on the first monitored road surface image is as follows: Based on the acquired first monitored road surface image, the first monitored road surface image is preprocessed by a grayscale processing unit, a filtering and denoising processing unit, and a histogram equalization processing unit to obtain a second monitored road surface image.

4. A method for real-time monitoring of asphalt road construction quality according to claim 1, characterized in that: The image balance characteristic information includes the image particle circularity factor and the image particle area discreteness. The specific extraction execution method of the image balance characteristic information is as follows: In the first step, the particles are separated from the background of the second monitored road image using image segmentation technology, and each particle is marked to obtain the image particle marking points, and the image particle area of ​​each monitored sub-area is calculated. Pa i , the calculation model is as follows: ,in, Pa i represents the image particle area of ​​the i-th monitoring sub-region, M Indicates the height of the image. N Indicates the width of the image. Indicates that the image of the i-th monitoring sub-area is at position The pixel value at ; The second monitored road surface image is introduced into the edge detection algorithm to identify the edge line of each particle in the second monitored road surface image, and the length of the curve is calculated as the perimeter of the edge line of the image particle by fitting a curve to the edge line of the image particle. Lp ; Read the image particle area of ​​the i-th monitoring sub-area Pa i and the perimeter of the edge line of the image particles in the i-th monitoring sub-area Lp i , calculate the circularity factor of the image particles in the i-th monitoring sub-area Prf i , the calculation model is as follows: ,in, represents pi; In the second step, the area of ​​each image particle is marked as , m represents the total number of image particles, j=1, 2, 3, ..., m, j is the number of each image particle; Calculate the discrete degree of image particle area, the calculation model is as follows: ,in, Pd i Indicates the discrete degree of the image particle area in the i-th monitoring sub-area, Pa ij Represents the area of ​​the jth image particle in the ith monitoring sub-region.

5. The method for real-time monitoring of asphalt road construction quality according to claim 1, characterized in that: The image density feature information includes the image road surface crack rate and the image road surface porosity. The specific extraction execution method of the image density feature information is as follows: The first step is to import the second monitored road surface image into the edge detection algorithm to identify the crack edge line in the second monitored road surface image, and use the threshold segmentation technology to convert the second monitored road surface image into a binary image, with the crack area being white and the background being black, and count the number of pixels in the crack area of ​​each monitored sub-area Nc i and the number of pixels in the monitored road image area of ​​each monitored sub-area Nt i , substituting it into the formula Get the pavement image crack rate of the i-th monitoring sub-area Cr i ; In the second step, the second monitored road surface image is divided into pore and non-pore areas by binarization processing, and the number of pixels in the pore area of ​​each monitored sub-area is counted. Np i and the number of pixels in the monitored road image area of ​​each monitored sub-area Nt i , substituting it into the formula Get the porosity of the road surface image in the i-th monitoring sub-area Pr i .

6. A method for real-time monitoring of asphalt road construction quality according to claim 1, characterized in that: The specific implementation method of the asphalt road leveling abnormality monitoring and analysis is as follows: S31: Reading the image particle circularity factor of the i-th monitoring sub-area in the image balance feature information Prf i , substituting it into the formula Get the image particle circularity deviation factor of the i-th monitoring sub-area Rd i ,in, Prf 0 represents the preset standard image particle circularity factor; S32: Reading the image particle area discreteness of the i-th monitoring sub-area in the image balance feature information Pd i , substituting it into the formula Get the coefficient of variation of the image particle area of ​​the i-th monitoring sub-area Cov i ,in, represents the average value of the particle area of ​​the image in the i-th monitoring sub-region; S33: Calculate the pavement smoothness balance coefficient of asphalt roads FEC , the calculation model is as follows: ,in, Rd 0 means the preset maximum value of the circularity deviation factor of the image particles. e represents a natural constant, n represents the total number of each monitoring sub-area, and i represents the number of each monitoring sub-area; S34: Extract the pavement smoothness balance coefficient of the asphalt road and compare it with the preset pavement smoothness balance coefficient threshold to evaluate the pavement smoothness balance of the asphalt road. If the pavement smoothness balance coefficient is greater than or equal to the preset pavement smoothness balance coefficient threshold, it is determined that there is no abnormality in the pavement smoothness of the asphalt road. If the pavement smoothness balance coefficient is less than the preset pavement smoothness balance coefficient threshold, it is determined that there is an abnormality in the pavement smoothness of the asphalt road, and a warning message is issued for data with abnormal judgment results.

7. A method for real-time monitoring of asphalt road construction quality according to claim 1, characterized in that: The specific implementation method of the asphalt road compaction abnormality monitoring and analysis is as follows: S41: Reading the pavement image crack rate of the ith monitoring sub-area in the image density feature information Cr i , substituting it into the formula Get the image crack deviation of the i-th monitoring sub-area Cd i ,in, Cr 0 indicates the preset maximum crack rate of the pavement image; S42: Calculate the pavement compactness evaluation coefficient of asphalt roads DEC , the calculation model is as follows: ,in, Pr i represents the porosity of the road surface image in the ith monitoring sub-area, e represents a natural constant, n represents the total number of each monitoring sub-area, and i represents the number of each monitoring sub-area; S43: Extract the pavement density evaluation coefficient of the asphalt road and compare it with the preset pavement density evaluation coefficient threshold to evaluate the pavement density of the asphalt road. If the pavement density evaluation coefficient is greater than or equal to the preset pavement density evaluation coefficient threshold, it is determined that there is no abnormality in the pavement density of the asphalt road. If the pavement density evaluation coefficient is less than the preset pavement density evaluation coefficient threshold, it is determined that there is an abnormality in the pavement density of the asphalt road, and a warning message is issued for data with abnormal judgment results.

8. The method for real-time monitoring of asphalt road construction quality according to claim 1, characterized in that: The specific implementation method of the asphalt road construction quality monitoring and analysis is as follows: S51: Extract the pavement smoothness balance coefficient of asphalt roads FEC and pavement compactness evaluation coefficient of asphalt roads DEC , monitor the construction quality of asphalt road projects, and analyze the construction quality evaluation index of asphalt roads QEI , the calculation model is as follows: ; S52: Assess the construction quality of the asphalt road project, extract the construction quality evaluation index of the asphalt road and compare it with the preset construction quality evaluation index threshold. If the construction quality evaluation index of the asphalt road is greater than or equal to the preset construction quality evaluation index threshold, the construction quality of the asphalt road project is judged to be qualified. If the construction quality evaluation index of the asphalt road is less than the preset construction quality evaluation index threshold, the construction quality of the asphalt road project is judged to be unqualified. Issue a warning message for the data judged to be unqualified, and output the asphalt road project construction quality monitoring results.

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