A real-time detection method for the flatness of asphalt pavement during construction

Abstract

The present invention discloses a real-time detection method for the smoothness of asphalt pavement during the construction process, which is used to detect the smoothness of the pavement in real time during the rolling process of the asphalt pavement by vehicles. It includes step S1: The processing module obtains the smoothness data of the pavement through the responsive smoothness measurement method, obtains the dynamic vertical acceleration of the vehicle according to the high-precision accelerometer, and calculates the root mean square of the acceleration based on the dynamic vertical acceleration. The real-time detection method for the smoothness of asphalt pavement during the construction process disclosed by the present invention can, under the hot state of the asphalt mixture, that is, during the rolling process of the asphalt pavement, detect the indicators such as the smoothness of the pavement in real time, and perform quantification and visualization, so as to provide reference for the roller driver and on-site technicians, discover abnormal conditions in the first time, and enable them to correct the unqualified sections such as smoothness in a targeted manner in a timely manner.

Description

Technical Field

[0001] The present invention belongs to the technical field of pavement evenness detection, and particularly relates to a method for real-time detection of the evenness of an asphalt pavement during the construction process. Background Art

[0002] During the actual paving process of an asphalt pavement, multiple rollers closely follow the paver to perform rolling operations on the pavement to ensure that indicators such as pavement evenness meet the requirements of the post-construction pavement quality acceptance. Various rollers adopt a driving strategy of traveling back and forth and repeatedly compressing, and there are often situations where sections with qualified quality are still rolled, while sections that do not meet the standards are easily overlooked. Asphalt mixture is plastic in the hot state and is easily affected by various factors such as human, equipment, and environment during the paving and rolling processes, resulting in phenomena such as undulations and bumps on the pavement. If such problems cannot be detected in a timely manner in the hot state, the unevenness of the pavement will become a fixed form and be difficult to repair, which will have a great impact on the driving comfort of drivers and passengers, and may even pose a safety hazard in severe cases.

[0003] Currently, the evaluation of pavement construction quality mainly adopts the method of detecting after completion of repair and compaction. It has the limitations of "taking points as the whole" and the passivity of "post-detection". If the process monitoring of indicators such as the evenness of asphalt concrete during the construction process cannot be achieved, and it is found that the pavement evenness is poor after the pavement cools down, it will result in high repair costs and poor social impacts.

[0004] Therefore, in view of the above problems, further improvements are made. Summary of the Invention

[0005] The main purpose of the present invention is to provide a method for real-time detection of the evenness of an asphalt pavement during the construction process, which can detect indicators such as the evenness of the pavement in real time, quantify and visualize them under the hot state of the asphalt mixture, that is, during the rolling process of the asphalt pavement, and then provide references for roller drivers and on-site technical personnel, discover abnormal conditions in the first time, and enable them to correct the sections that do not meet the standards such as evenness in a targeted manner.

[0006] Another purpose of the present invention is to provide a method for real-time detection of the evenness of an asphalt pavement during the construction process, which uses advanced pavement quality detection equipment to obtain evenness quality data, adopts high-precision (centimeter-level) positioning technology to obtain the compaction trajectory of the roller, and performs coordinate transformation by establishing a corresponding relationship model between spatial coordinates and plane coordinates, and distinguishes and marks them with different colors on the plane coordinate system, so as to realize the real-time visual display of the evenness quality, thereby informing the roller driver to adjust the rolling method and reminding the on-site technical personnel to make timely responses.

[0007] To achieve the above object, the present invention provides a method for real-time detection of the flatness during the construction of an asphalt pavement, which is used to detect the flatness of the pavement in real time during the rolling process of the asphalt pavement by a vehicle (roller), and includes the following steps:

[0008] Step S1: The processing module obtains the flatness data of the pavement through a responsive flatness measurement method, obtains the dynamic vertical acceleration of the vehicle according to a high-precision accelerometer, and calculates the root mean square of the acceleration based on the dynamic vertical acceleration;

[0009] Step S2: The processing module establishes a conversion model between the spatial coordinates and the actual plane distance coordinates to display the actual distance in the plane coordinates;

[0010] Step S3: The processing module visually displays the obtained international roughness index IRI, so as to adjust the rolling of the pavement.

[0011] As a further preferred technical solution of the above technical solution, step S1 is specifically implemented as the following steps:

[0012] Step S1.1: Perform outlier test and rejection processing on a set of obtained acceleration values A = {A 1 , A 2 , …, A N} through the following formula. The formula is:

[0013]

[0014] Among them, is the average acceleration, std is the acceleration standard deviation, N is the number of acceleration values per second, that is, the accelerometer output frequency (preferably 300). If A i is an outlier, then use to replace and remove the outlier;

[0015] Step S1.2: Perform filtering processing on a set of acceleration values A' = {A' 1 , A' 2 , …, A' N} obtained after outlier test and rejection processing to obtain the sliding filter smoothed data of the acceleration. The formula is:

[0016]

[0017] Among them, a' i is the i-th acceleration value after filtering, N is the number of acceleration values, and m is the number of acceleration values within the filtering window (preferably an odd number 5);

[0018] Step S1.3: Calculate the filtered acceleration values using the following formula to obtain the root mean square acceleration, where the formula is:

[0019]

[0020] where a′ i is the i-th acceleration value after filtering, is the average acceleration after filtering, and N is the number of acceleration values;

[0021] Step S1.4: Calculate the obtained root mean square acceleration using the following formula to obtain the International Roughness Index IRI, where the formula is:

[0022] IRI = A + B * RMSVA;

[0023] where A and B are conversion coefficients obtained after calibration, both of which are obtained from the calibration process of the detection equipment carried by the vehicle.

[0024] As a further preferred technical solution of the above technical solution, step S2 is specifically implemented as the following steps:

[0025] Step S2.0: Convert the geodetic coordinates to spatial coordinates;

[0026] Step S2.1: Use the latitude obtained by GPS measurement as the independent variable x and the longitude as the dependent variable y to calculate the rotation matrix of the vehicle driving direction;

[0027] Step S2.2: Calculate the true distance of the x coordinate;

[0028] Step S2.3: Calculate the true distance of the y coordinate.

[0029] As a further preferred technical solution of the above technical solution, step S2.0 is specifically implemented as the following steps:

[0030] Step S2.0.1: The geodetic coordinate data measured by GPS is the coordinate value in the geodetic coordinate system. First, convert the data to the space rectangular coordinate system, that is:

[0031]

[0032] where L is the longitude, B is the latitude, H is the geodetic height, a is the semi-major axis of the earth ellipsoid (take 6378137.0000m), and b is the semi-minor axis of the earth ellipsoid (take 6356752.3141m).

[0033] As a further preferred technical solution of the above technical solution, step S2.1 is specifically implemented as the following steps:

[0034] Step S2.1.1: Obtain the longitude and latitude data P of the vehicle starting point 0 (x 0 ,y 0 ). Obtain 4 groups of longitude and latitude data at intervals of 1 meter along the vehicle driving direction, and subtract the initial position P 0 from all of them, so that the trajectory starts from the coordinate origin. The 4 groups of data are denoted as P 1 (x 1 ,y 1 ), P 2 (x 2 ,y 2 ), P 3 (x 3 ,y 3 ), P 4 (x 4 ,y 4 );

[0035] Step S2.1.2: Construct a fitted straight line y = kx for the driving trajectory, where the driving direction in the plane coordinate system is from bottom to top. Therefore, calculate the rotation matrix according to the trajectory and correct and transform the longitude and latitude data into plane coordinates.

[0036] As a further preferred technical solution of the above technical solution, the specific implementation of the trajectory calculation rotation matrix in Step S2.1.2 is as follows:[[]]

[0037] Step S2.1.2.1: When the driving trajectory is in the first and fourth quadrants, the rotation matrix is:[[]]

[0038]

[0039] where, when α is in the first quadrant = when α is in the fourth quadrant =

[0040] Step S2.1.2.2: When the driving trajectory is in the second and third quadrants, the rotation matrix is:[[]]

[0041]

[0042] where when α is in the second quadrant = when α is in the third quadrant =

[0043] Step S2.1.2.3: Correct the plane coordinates according to the obtained rotation matrix to obtain the corrected coordinates (x′, y′);

[0044]

[0045] As a further preferred technical solution of the above technical solution, step S2.2 is specifically implemented as the following steps:

[0046] Step S2.2.1: Taking the starting point P 0 (x 0 , y 0 ) as the origin, obtain 4 sets of longitude and latitude data at intervals of 1 meter along the direction perpendicular to the driving direction, and subtract the initial position P 0 from all of them, and perform coordinate rotation transformation on the 4 sets of data according to the method in step S2.1. The final result is denoted as P' 1 (x' 1 , y' 1 ), P' 2 (x' 2 , y' 2 ), P' 3 (x' 3 , y' 3 ), P' 4 (x' 4 , y' 4 ), where x' 1 , x' 2 , x' 3 , x' 4 are the transformed coordinates, and the actual corresponding distances are 1, 2, 3, 4 meters. Therefore, construct the true distance coordinate model as follows:

[0047]

[0048] where, x true is the true distance coordinate, x is the rotated coordinate. According to the least squares principle, solve the parameters:

[0049]

[0050] where, is the mean value of the rotated coordinates, is the mean value of the true values.

[0051] As a further preferred technical solution of the above technical solution, step S2.3 is specifically implemented as the following steps:

[0052] Step S2.3.1: After calculating the x value according to step S2.2, calculate the position y value of this point according to the position true of the previous point and the actual distance. The process is as follows:

[0053] i. Calculate the distance D between the two positions through the following formula based on the longitudes and latitudes obtained from the two points:

[0054]

[0055] Among them, L is the longitude, B is the latitude, H is the geodetic height, and r is the average radius of the earth ellipsoid.

[0056] ii. Calculate the y-axis spacing d according to the distance D and the x value y Value:

[0057]

[0058] iii. Calculate according to the driving trajectory:

[0059]

[0060] Among them, y′ i is the y value of the rotated coordinate, and is the coordinate origin, so the value is 0.

[0061] As a further preferred technical solution of the above technical solution, step S3 is specifically implemented as the following steps:

[0062] Step S3.1: Calculate the real-time dynamic threshold of the international roughness index IRI, and the calculation process is as follows:

[0063] For the IRI data set at a certain moment, denote R = {R 1 , R 2 , …, R N}, arrange the N data in ascending order of numerical value. If there are multiple data for a certain value, only take one. Denote r = {r 1 , r 2 , …, r n}, where r 1 < r 2 < … < r n , n ≤ N. Calculate the cumulative frequency of each data in r in R, and denote P = {P 1 , P 2 , …, P n}. Then divide the threshold according to P:

[0064]

[0065] Step S3.2: Visualize the international roughness index IRI. Adopt the visualization method of different color rectangular frames. The width of the rectangle is the actual width of the roller of the vehicle (road roller), and the length is the interval of the y value of the ordinate. The filling color of the rectangle is divided into three categories according to the threshold calculated in step S3.1, as follows:

[0066]

[0067] As a further preferred technical solution of the above technical solution, before step S1.1, there is also step S1.0: synchronize the time of the GPS and the accelerometer. Description of the Drawings

[0068] Figure 1 is a visual schematic diagram of a method for real-time detection of the flatness of an asphalt pavement construction process according to the present invention.

[0069] Figure 2 is a time synchronization schematic diagram of a method for real-time detection of the flatness of an asphalt pavement construction process according to the present invention. Detailed Embodiments

[0070] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art can think of other obvious variations. The basic principles defined in the following description of the present invention can be applied to other embodiments, variations, improvements, equivalent solutions, and other technical solutions that do not depart from the spirit and scope of the present invention.

[0071] In the preferred embodiment of the present invention, those skilled in the art should note that the vehicles, rollers, etc. involved in the present invention can be regarded as prior art.

[0072] Preferred Embodiment.

[0073] The present invention discloses a method for real-time detection of the flatness of an asphalt pavement construction process, which is used to detect the flatness of the pavement in real time during the process of the asphalt pavement being rolled by a vehicle (roller), and includes the following steps:

[0074] Step S1: The processing module obtains the flatness data of the road surface by the responsive flatness measurement method, obtains the dynamic vertical acceleration of the vehicle according to the high-precision accelerometer, and calculates the root mean square of the acceleration according to the dynamic vertical acceleration;

[0075] Step S2: The processing module establishes a conversion model between the spatial coordinates and the actual plane distance coordinates to display the actual distance in the plane coordinates;

[0076] Step S3: The processing module visually displays the obtained international roughness index IRI, so as to adjust the rolling of the road surface.

[0077] Specifically, step S1 is specifically implemented as the following steps:

[0078] Step S1.1: Perform an outlier test and rejection process on a set of obtained acceleration values A = {A 1 , A 2 , …, A N} through the following formula:

[0079]

[0080] Among them, is the average acceleration, std is the standard deviation of acceleration, N is the number of acceleration values per second, that is, the accelerometer output frequency (preferably 300). If A i is an outlier, then is used to replace and eliminate the outlier;

[0081] Step S1.2: Test the outlier and filter a set of acceleration values A′ = {A′ 1 , A′ 2 , …, A′ N} obtained after the elimination process to obtain the sliding filtered smooth data of the acceleration. The formula is:

[0082]

[0083] where a′ i is the i-th acceleration value after filtering, N is the number of acceleration values, and m is the number of acceleration values in the filtering window (preferably an odd number 5);

[0084] Step S1.3: Calculate the filtered acceleration values through the following formula to obtain the root mean square of acceleration. The formula is:

[0085]

[0086] where a′ i is the i-th acceleration value after filtering, is the average acceleration after filtering, and N is the number of acceleration values;

[0087] Step S1.4: Calculate the obtained root mean square of acceleration through the following formula to obtain the international roughness index IRI. The formula is:

[0088] IRI = A + B * RMSVA;

[0089] where A and B are conversion coefficients obtained after calibration, both of which are obtained from the calibration process of the detection equipment carried by the vehicle.

[0090] More specifically, step S2 is specifically implemented as the following steps:

[0091] Step S2.0: Convert the geodetic height coordinates to space coordinates;

[0092] Step S2.1: Use the latitude obtained by GPS measurement as the independent variable x and the longitude as the dependent variable y to calculate the rotation matrix of the vehicle driving direction;

[0093] Step S2.2: Calculate the true distance of the x coordinate;

[0094] Step S2.3: Calculate the true distance of the y coordinate.

[0095] Preferably, step S2.0 is specifically implemented as the following steps:

[0096] Step S2.0.1: The geodetic height data measured by GPS are coordinate values in the geodetic coordinate system. First, convert the data to the space rectangular coordinate system, that is:

[0097]

[0098] where L is the longitude, B is the latitude, H is the geodetic height, a is the semi-major axis of the earth ellipsoid (take 6378137.0000 m), and b is the semi-minor axis of the earth ellipsoid (take 6356752.3141 m).

[0099] Furthermore, step S2.1 is specifically implemented as the following steps:

[0100] Step S2.1.1: Obtain the vehicle starting point longitude and latitude data P 0 (x 0 , y 0 ). Along the vehicle driving direction, obtain 4 groups of longitude and latitude data at intervals of 1 meter each, and subtract the initial position P 0 , so that the trajectory starts from the coordinate origin. The 4 groups of data are denoted as P 1 (x 1 , y 1 ), P 2 (x 2 , y 2 ), P 3 (x 3 , y 3 ), P 4 (x 4 , y 4 );

[0101] Step S2.1.2: Construct a fitting straight line for the driving trajectory y = kx, where The driving direction in the plane coordinate system is from bottom to top. Therefore, calculate the rotation matrix according to the trajectory and correct and transform the longitude and latitude data into plane coordinates.

[0102] Even further, the trajectory calculation rotation matrix in step S2.1.2 is specifically implemented as the following steps:

[0103] Step S2.1.2.1: When the driving trajectory is in the first and fourth quadrants, the rotation matrix is:

[0104]

[0105] where when α is in the first quadrant = when α is in the fourth quadrant =

[0106] Step S2.1.2.2: When the driving trajectory is in the second and third quadrants, the rotation matrix is:

[0107]

[0108] where when α is in the second quadrant = when α is in the third quadrant =

[0109] Step S2.1.2.3: Correct the plane coordinates according to the obtained rotation matrix to obtain the corrected coordinates (x′, y′);

[0110]

[0111] Preferably, step S2.2 is specifically implemented as the following steps:

[0112] Step S2.2.1: Taking the starting point P 0 (x 0 , y 0 ) as the origin, obtain 4 groups of longitude and latitude data at intervals of 1 meter along the vertical direction of the driving direction, and subtract the initial position P 0 from all of them, and perform coordinate rotation transformation on the 4 groups of data according to the method in step S2.1. The final result is denoted as P′ 1 (x′ 1 , y′ 1 ), P′ 2 (x′ 2 , y′ 2 ), P′ 3 (x′ 3 , y′ 3 ), P′ 4 (x′ 4 , y′ 4 ), where x′ 1 , x′ 2 , x′ 3 , x′ 4 are the transformed coordinates, and the actual corresponding distances are 1, 2, 3, 4 meters. Therefore, construct the true distance coordinate model as follows:

[0113]

[0114] where, x true is the true distance coordinate, x is the rotated coordinate. According to the least squares principle, solve the parameters:

[0115]

[0116] Among them, is the average value of the rotated coordinates, is the average value of the true value.

[0117] Preferably, step S2.3 is specifically implemented as the following steps:

[0118] Step S2.3.1: After obtaining the x value calculated in step S2.2, according to the position of the previous point and the actual distance, calculate the position y true value, and the process is as follows:

[0119] i. Calculate the distance D between two positions through the following formula based on the longitude and latitude obtained from two points:

[0120]

[0121] Among them, L is the longitude, B is the latitude, H is the geodetic height, and r is the average radius of the earth ellipsoid.

[0122] ii. Calculate the y-axis spacing d y value according to the distance D and the x value:

[0123]

[0124] iii. Calculate according to the driving trajectory:

[0125]

[0126] Among them, y′ i is the y value of the rotated coordinate, and is the coordinate origin, so the value is 0.

[0127] Preferably, step S3 is specifically implemented as the following steps:

[0128] Step S3.1: Perform real-time dynamic threshold calculation on the international roughness index IRI, and the calculation process is as follows:

[0129] For the IRI data set at a certain moment, denote R = {R 1 , R 2 , …, R N}, arrange the N data in ascending order of numerical value. If there are multiple data for a certain value, only take one. Denote r = {r 1 , r 2 , …, r n}, where r 1 < r 2 < … < r n, for \(n\leq N\), calculate the cumulative frequency of each data in \(r\) when it appears in \(R\), and denote \(P = \{P 1 ,P 2 ,…,P n \}, then the threshold is divided according to \(P\) as follows:

[0130]

[0131] Step S3.2: Visualize the international roughness index IRI. Adopt the visualization method of different - colored rectangular frames. The width of the rectangle is the actual width of the compaction wheel of the vehicle (road roller), and the length is the interval of the ordinate \(y\) value. The filling color of the rectangle is divided into three categories according to the threshold calculated in Step S3.1, as follows:

[0132]

[0133] As Figure 1 shown, the first color is preferably green (representing excellent), the second color is preferably yellow (representing medium), and the third color is preferably red (representing poor).

[0134] Preferably, as Figure 2 shown, before Step S1.1, there is also Step S1.0: Synchronize the time of the GPS and the accelerometer.

[0135] It is worth mentioning that the technical features such as the vehicle and road roller involved in this invention patent application should be regarded as the prior art. The specific structures, working principles, and possible control methods and spatial layout methods involved in these technical features can be selected conventionally in this field and should not be regarded as the inventive points of this invention patent. This invention patent will not be further specifically elaborated.

[0136] For those skilled in the art, it is still possible to modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A real-time detection method for the smoothness of asphalt pavement construction process, which is used to detect the smoothness of the pavement in real time during the rolling process of the asphalt pavement by vehicles. Characterized in that, It includes the following steps: Step S1: The processing module obtains the smoothness data of the pavement through the responsive smoothness measurement method, obtains the dynamic vertical acceleration of the vehicle according to the high-precision accelerometer, and calculates the root mean square of acceleration according to the dynamic vertical acceleration. Step S2: The processing module establishes a conversion model between the space coordinate and the actual plane distance coordinate to display the actual distance in the plane coordinate. Step S2 is specifically implemented as the following steps: Step S2.0: Convert the geodetic height coordinates to space coordinates. Step S2.1: Take the latitude obtained by GPS measurement as the independent variable x and the longitude as the dependent variable y, so as to calculate the rotation matrix of the vehicle driving direction. Step S2.2: Calculate the true distance of the x coordinate. Step S2.3: Calculate the true distance of the y coordinate. Step S2.1 is specifically implemented as the following steps: Step S2.1.1: Obtain the longitude and latitude data P of the vehicle starting point 0 (x 0 , y 0 ). Obtain 4 groups of longitude and latitude data at intervals of 1 meter along the vehicle driving direction, and subtract the initial position P 0 from all of them, so that the trajectory starts from the coordinate origin. The 4 groups of data are denoted as P 1 (x 1 , y 1 ), P 2 (x 2 , y 2 ), P 3 (x 3 , y 3 ), P 4 (x 4 , y 4 ); Step S2.1.2: Construct the fitting straight line of the driving trajectory y = kx, where The driving direction in the plane coordinate system is from bottom to top. Therefore, calculate the rotation matrix according to the trajectory and correct and convert the longitude and latitude data into plane coordinates; Step S2.2 is specifically implemented as the following steps: Step S2.2.1: Taking the starting point P 0 (x 0 ,y 0 ) as the origin, obtain 4 sets of longitude and latitude data every 1 meter along the vertical direction of the driving direction, and subtract the initial position P 0 from all of them, and perform coordinate rotation transformation on the 4 sets of data according to the method in step S2.

1. The final result is denoted as P′ 1 (x′ 1 ,y′ 1 ). P′ 2 (x′ 2 ,y′ 2 ), P′ 3 (x′ 3 ,y′ 3 ), P′ 4 (x′ 4 ,y′ 4 ), where x′ 1 , x′ 2 , x′ 3 , x′ 4 are the transformed coordinates, and the actual corresponding distances are 1, 2, 3, and 4 meters. Therefore, construct the true distance coordinate model as follows: where x true is the true distance coordinate, x' is the rotated coordinate, and according to the least squares principle, the parameters are solved as follows: Among them, is the mean of the rotated coordinates, is the mean of the true values; Step S2.3 is specifically implemented as the following steps: Step S2.3.1: After obtaining the x value calculated in step S2.2, calculate the position y of this point based on the position of the previous point and the actual distance. The process is as follows: true value, and the process is as follows: i. Calculate the distance D between two positions according to the longitude and latitude obtained by two points through the following formula: Where L is the longitude, B is the latitude, H is the geodetic height, and r is the average radius of the earth ellipsoid. ii. Calculate the y-axis spacing d based on the distance D and the x value y Value: iii. Calculate according to the driving trajectory: where y′ i is the y value of the rotated coordinate, and is the coordinate origin, so the value is 0; Step S3: The processing module visually displays the obtained international roughness index IRI, so as to adjust the rolling of the pavement.

2. A real-time detection method for the smoothness of asphalt pavement construction process according to claim 1, Characterized in that, Step S1 is specifically implemented as the following steps: Step S1.1: Perform an outlier test and rejection process on a set of obtained acceleration values A = {A 1 , A 2 , …, A N} using the following formula: Among them, is the average acceleration value, std is the standard deviation of acceleration, and N is the number of acceleration values per second, that is, the accelerometer output frequency. If A i is an outlier, then is used for replacement to remove the outlier; Step S1.2: A set of acceleration values ​​A′={A′ 1 ,A′ 2 ,…,A′ N } to filter the acceleration to obtain the sliding filter smoothing data. The formula is: where a′ i is the i-th acceleration value after filtering, N is the number of acceleration values, and m is the number of acceleration values within the filtering window; Step S1.3: Calculate the filtered acceleration value through the following formula to obtain the root mean square of acceleration. The formula is: where a′ i is the i-th acceleration value after filtering, is the average acceleration value after filtering, and N is the number of acceleration values; Step S1.4: Calculate the obtained root mean square of acceleration through the following formula to obtain the international roughness index IRI. The formula is: IRI = A + B * RMSVA; Where A and B are conversion coefficients obtained after calibration, both of which are obtained from the calibration process of the detection equipment carried by the vehicle.

3. A real-time detection method for the smoothness of asphalt pavement construction process according to claim 2, Characterized in that, Step S2.0 is specifically implemented as the following steps: Step S2.0.1: The geodetic height data measured by GPS is the coordinate value in the geodetic coordinate system. First, convert the data to the space rectangular coordinate system, that is: Where L is the longitude, B is the latitude, H is the geodetic height, a is the semi-major axis of the earth ellipsoid, and b is the semi-minor axis of the earth ellipsoid.

4. A real-time detection method for the smoothness of asphalt pavement construction process according to claim 3, Characterized in that, The trajectory calculation rotation matrix in step S2.1.2 is specifically implemented as the following steps: Step S2.1.2.1: When the driving trajectory is in the first and fourth quadrants, the rotation matrix is: where, when α is in the first quadrant = when α is in the fourth quadrant = Step S2.1.2.2: When the driving trajectory is in the second and third quadrants, the rotation matrix is: When α is in the second quadrant = When α is in the third quadrant = Step S2.1.2.3: Correct the plane coordinates according to the obtained rotation matrix to obtain the corrected coordinates (x′, y′).

5. A real-time detection method for the smoothness of an asphalt pavement construction process according to claim 4, characterized in that, Step S3 is specifically implemented as the following steps: Step S3.1: Calculate the real-time dynamic threshold of the international roughness index IRI, and the calculation process is as follows: For the IRI dataset at a certain moment, let \(R = \{R 1 ,R 2 ,\cdots,R N \}\). Arrange the \(N\) data in ascending order of value. If there are multiple data with the same value, only take one. Let \(r=\{r 1 ,r 2 ,\cdots,r n \}\), where \(r 1 < r 2 < \cdots < r n \), and \(n\leq N\). Calculate the cumulative frequency of each data in \(r\) in \(R\). Let \(P = \{P 1 ,P 2 ,\cdots,P n \}\). Then, the threshold is divided according to \(P\) as follows: Step S3.2: Visualize the international roughness index IRI. Adopt the visualization method of different color rectangular frames. The width of the rectangle is the actual width of the vehicle's pressing wheel, and the length is the interval of the ordinate y value. The filling color of the rectangle is divided into three categories according to the threshold calculated in Step S3.1, as follows:

6. A real-time detection method for the smoothness of an asphalt pavement construction process according to claim 5, characterized in that, Before Step S1.1, there is also Step S1.0: Synchronize the time of the GPS and the accelerometer.

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