High dynamic deformation measurement system and method based on laser cross-section profile differential scanning

Abstract

The application provides a high dynamic deformation measurement system and method based on laser profile differential scanning. The measurement system is composed of a profile measurement unit, a calibration unit, an error compensation unit and a data processing unit, wherein the profile measurement unit comprises a multi-view structured light sensor module, a speed sensor module and a ranging module; the calibration unit comprises a synchronous control module, an installation and adjustment module and a common tooth profile calibration target; the error compensation unit comprises a vibration detection module, an attitude detection module and a temperature detection module; and the data processing unit is a device with programming and calculation functions. The measurement method comprises the following steps: (1) laser plane and image plane calibration; (2) global coordinate system unification; (3) point cloud data acquisition; (4) error compensation; (5) deformation time history model construction; and (6) global optimization of maximum deformation. The measurement system and method can realize accurate expression of three-dimensional space of pavement micro-deformation under load and global optimization of maximum value.

Description

Technical Field

[0001] This invention relates to the field of road surface inspection technology, specifically to a high dynamic deformation measurement system and method based on laser cross-sectional profile differential scanning. Background Technology

[0002] Road surface load-bearing capacity is a crucial guarantee for road traffic safety; almost all road collapses are caused by an imbalance between load-bearing capacity and load. Road surface micro-deformation under vehicle loads is an important parameter for evaluating road surface load-bearing capacity and a "barometer" of road infrastructure safety. Given the massive scale of highway networks and the coexistence of multiple safety risks, high-speed dynamic detection of road surface micro-deformation at the road network level can significantly improve the efficiency of road load-bearing capacity detection, providing vital data support for road network-level safety risk assessment.

[0003] Traditional road structure load-bearing capacity testing uses the Beckman beam method, but this method has extremely low testing efficiency. Subsequently, the assessment of highway pavement structure mainly developed into the Falling Weight Deflectometer (FWD) testing method. However, the FWD method has low single-point operation efficiency (testing speed of about 5 km / h) and is still difficult to meet the needs of current high-speed dynamic testing at the road network level. Compared with traditional static or low-speed automatic pavement micro-deformation measurement technology, researchers have successively developed measurement equipment based on spatial repetition method to directly measure the micro-deformation of high-speed pavement, such as RWD (Real Wheel Load Deflections) and RDT (Road Deflection Tester). Another indirect measurement method, represented by TSD (Traffic Speed ​​Deflectometer) and LDD (Laser Dynamic Deflectometer), uses the Doppler effect to measure the vertical deformation rate of the pavement, and obtains the vertical deformation through velocity conversion and slope integration.

[0004] The above-mentioned measurement methods or devices can only achieve single-point measurement of micro-vertical deformation of the road surface or deformation curve measurement within a response time interval under a certain load, and have significant limitations in global optimization of the maximum vertical deformation of the road surface under load.

[0005] In summary, this invention provides a high-dynamic deformation measurement system and method based on laser profile differential scanning for high-precision three-dimensional spatial measurement of pavement dynamic micro-deformation under high-speed moving loads. This method not only enables high-dynamic, microsecond-level three-dimensional spatial measurement of pavement micro-deformation with wide swaths, providing a solution for the global determination of the maximum value of pavement micro-deformation under load, but also provides a theoretical basis and technical support for the metrological calibration of existing pavement micro-deformation measuring instruments. Summary of the Invention

[0006] To address the problems existing in the prior art, this invention provides a high dynamic deformation measurement system and method based on differential scanning of laser cross-sectional profiles.

[0007] On the one hand, the present invention provides a high dynamic deformation measurement system based on laser cross-sectional profile differential scanning, including a cross-sectional profile measurement unit, a calibration unit, an error compensation unit and a data processing unit.

[0008] The cross-sectional profile measurement unit includes a multi-view structured light sensor module, a velocity sensing module, and a ranging module. The multi-view structured light sensor module is fixedly mounted on the rigid crossbeam of the moving carrier and consists of several longitudinally and transversely arranged line structured light sensors, with no fewer than three line structured light sensors. Each line structured light sensor independently acquires the cross-sectional profile perpendicular to the road surface. The velocity sensing module is used to measure the velocity of the moving carrier, and the ranging module is used to measure the displacement of the moving carrier.

[0009] The calibration unit includes a synchronization control module, an assembly and adjustment module, and a common tooth-shaped calibration target. The synchronization control module is based on multi-source sensor synchronization control technology to realize synchronous acquisition of multi-source sensor data. The assembly and adjustment module is an assembly and adjustment tool used for precise installation of the cross-sectional profile measurement unit. The common tooth-shaped calibration target is a special calibration target designed for calibrating the mutual positional relationship between the sensors in the multi-view structured light sensor module of the cross-sectional profile measurement unit.

[0010] The error compensation unit includes a vibration detection module, an attitude detection module, and a temperature detection module. The vibration detection module is fixedly mounted on the multi-view structured light sensor module, forming a rigid connection, and is used for vibration compensation of the cross-sectional profile measurement unit. The attitude detection module is fixedly mounted on the rigid crossbeam of the moving carrier and is used for angle compensation of the cross-sectional profile measurement unit. The temperature detection module is fixedly mounted on the rigid crossbeam of the moving carrier and is used for temperature compensation of the cross-sectional profile measurement unit.

[0011] The data processing unit is a device with programming and calculation functions.

[0012] On the other hand, the present invention provides a high dynamic deformation measurement method based on differential scanning of laser cross-sectional profiles, comprising the following steps:

[0013] (1) Calibration of the laser plane and the image plane;

[0014] (2) Global coordinate system one;

[0015] (3) Point cloud data acquisition;

[0016] (4) Error compensation;

[0017] (5) Construction of deformation time history model;

[0018] (6) Global optimization for maximum deformation.

[0019] Specifically, the laser plane and image plane calibration in step (1) refers to obtaining the laser plane equation in the camera coordinate system based on the mapping relationship between the feature points of the laser plane and the camera image plane, that is, determining the relative relationship between the laser plane and the camera coordinate system in a single line structured light sensor.

[0020] The global coordinate system mentioned in step (2) refers to determining the relative positions of multiple line structured light sensors. The coordinate system of any one of the line structured light sensors is defined as the global coordinate system, and the relative positions of the coordinate systems of the other line structured light sensors to the global coordinate system are described using rotation and translation matrices. Before establishing the global coordinate system, the initial installation pose of each line structured light sensor is ensured using the calibration unit's assembly and adjustment module; the synchronous data acquisition of each line structured light sensor is controlled using the calibration unit's synchronization control module; and the rotation and translation matrices are obtained using the common toothed calibration target of the calibration unit.

[0021] The point cloud data acquisition in step (3) refers to converting the two-dimensional image coordinates of the pixels into three-dimensional coordinates of the spatial points after step (1) and step (2), and stitching together the data point cloud of each image with the information of the velocity sensing module and the ranging module to obtain the point cloud data of each line structured light sensor in the global coordinate system.

[0022] The error compensation mentioned in step (4) refers to using the data obtained by the vibration detection module, attitude detection module and temperature detection module of the above error compensation unit to correct the point cloud data in step (3).

[0023] The deformation time history model construction mentioned in step (5) refers to performing time-domain difference operations on the deformation point cloud data of a cross section based on the deformation cloud points of a cross section at different times, and establishing the dynamic deformation of the cross section as time changes.

[0024] The global optimization of maximum deformation in step (6) refers to optimizing the maximum value of micro-deformation in three-dimensional space by combining the three-dimensional dynamics theory and pavement elasticity characteristics with the deformation time history model obtained in step (5).

[0025] Further, step (2) specifically involves: designing and processing a toothed calibration target with specific geometric shape and dimensional accuracy, ensuring that the distance between the tooth tip and tooth root is strictly consistent and that a certain angle is formed between adjacent teeth; building a calibration platform to ensure that the position of the toothed calibration target can be adjusted; determining the relationship between the coordinate system of the toothed calibration target and the coordinate system of each line structured light sensor based on feature matching, thereby realizing a global coordinate system.

[0026] Furthermore, step (3) specifically involves: assuming a point on the road surface is... It is constantly scanned by the laser plane of a certain structured light sensor, let its coordinates be... The equation of the laser plane in a structured optical sensor is: (in , , , (These are the coefficients of the plane equation), and the camera focal length in the line structured light sensor is... The length of a unit pixel is The width of a unit pixel is The center coordinates of the camera image coordinate system are The scaling factor is The rotation matrix of the line structured light sensor relative to the global coordinate system is: The translation matrix is Solving equation (1) yields the coordinates of the point in the global coordinate system. .

[0027] (1)

[0028] By combining the information from the velocity sensing module and the ranging module, all measurement points at different times are aggregated to obtain point cloud data of each line structured light sensor.

[0029] Further, step (5) specifically involves: using This will be explained using a line structured light sensor as an example. Point cloud data obtained from a line structured light sensor is used to reconstruct a 3D road map. The point cloud set obtained by the line structured light sensor is For any point ,definition for The surface normal vector pointing inwards at the point. A Gaussian filter is used. and normal vector The convolution calculation yields gradient space :

[0030] (2)

[0031] in , for Translation transformation after flipping; Here are the coordinates of the center point of the Gaussian filter. Standard deviation, These are the coordinates of the point to be filtered.

[0032] To achieve better gradient space solutions for discrete samples, a partitioning approximation method is adopted. The space is divided into different surface areas .make for At one of the points, we have , express The location, for If the normal vector information is obtained at a certain point, then equation (2) can be transformed into:

[0033] (3)

[0034] Establish the Poisson equation Find the indicator function After extracting the isosurface, the process is complete. 3D reconstruction of scanning data from a line structured light sensor, assuming its surface equation is... Similarly, the reconstructed surface of other line-structured optical sensors can be obtained. .

[0035] Next, construct the equation for the difference surface. Using the timestamp as a reference, , , ... Align the surfaces constructed at the specified locations to construct difference surfaces. Then there is

[0036] (4)

[0037] Each point in the difference equation represents the relative deformation at different times and locations. Since each measurement point is associated with time information, the dynamic deformation of the cross-section over time can be obtained by combining the difference equation. The three-dimensional distribution of the relative deformation in space can also be obtained from the difference surface equation.

[0038] Further, step (6) specifically involves: based on three-dimensional dynamic simulation, using time-frequency analysis, converting the three-dimensional dynamic problem into a plane strain problem with multiple discrete wavenumbers, meshing the road cross section, establishing the elastic properties of different base layers of the road based on elastic and viscoelastic constitutive equations, constructing a three-dimensional deformation surface model, and combining the differential surface constructed in step (5) to optimally estimate the parameters of the three-dimensional deformation surface, thereby achieving global optimization for maximum deformation.

[0039] This invention provides a high-dynamic deformation measurement system and method based on differential scanning of laser cross-sectional profiles. Firstly, the system and method fully consider the influence of environmental factors such as vibration, attitude, and temperature on the measurement results, and design an error compensation unit to improve the accuracy of the original 3D point cloud data. Secondly, in the 3D reconstruction process, the concept of differential surfaces is adopted, breaking through the limitations of expressing micro-deformation in 3D space. Finally, the 3D dynamic model and the constitutive equations of road surface characteristics are combined, making the theoretical basis for parameter estimation of differential surfaces more sufficient and the global optimization results more accurate. The system and method provided by this invention can not only realize high-dynamic, wide-swath, microsecond-level 3D spatial measurement of road surface micro-deformation, providing a solution for the global search of the maximum value of road surface micro-deformation under load, but also provide a theoretical basis and technical support for the metrological calibration of existing road surface micro-deformation measuring instruments. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 This is a diagram of the high dynamic deformation measurement system based on laser cross-sectional profile differential scanning provided in an embodiment of the present invention.

[0042] Figure 2 This is a schematic diagram of a multi-view structured light sensor module provided in an embodiment of the present invention.

[0043] Figure 3 The flowchart illustrates the three-dimensional spatial deformation measurement using the high dynamic deformation measurement system based on laser profile differential scanning, as provided in this embodiment of the invention.

[0044] Figure 4 This is a schematic diagram of a global coordinate system based on a common tooth-shaped calibration target provided in an embodiment of the present invention.

[0045] Figure 5 This is a schematic diagram illustrating the construction process of the deformation time history model provided in an embodiment of the present invention. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0047] The following embodiments, in conjunction with the accompanying drawings, will provide a detailed description of the high dynamic deformation measurement system and method based on laser cross-sectional profile differential scanning proposed in this application.

[0048] Figure 1 This is a diagram illustrating the composition of a high dynamic deformation measurement system based on differential scanning of laser cross-sectional profiles provided in an embodiment of the present invention. Figure 1 As shown, it includes: cross-sectional profile measurement unit 1, calibration unit 2, error compensation unit 3, and data processing unit 4.

[0049] The cross-sectional profile measurement unit 1 includes a multi-view structured light sensor module 1-1, a velocity sensing module 1-2, and a ranging module 1-3; wherein the multi-view structured light sensor module 1-1 is fixedly installed on the rigid crossbeam of the moving carrier.

[0050] The calibration unit 2 includes a synchronization control module 2-1, an assembly and adjustment module 2-2, and a common tooth-shaped calibration target 2-3; wherein the synchronization control module 2-1 is based on multi-source sensor synchronization control technology to realize synchronous acquisition of multi-source sensor data; the assembly and adjustment module 2-2 is an assembly and adjustment tool used for precise installation of the cross-sectional profile measurement unit; the common tooth-shaped calibration target 2-3 is a special calibration target designed for calibrating the mutual positional relationship between the sensors in the multi-view structured light sensor module of the cross-sectional profile measurement unit.

[0051] The error compensation unit 3 includes a vibration detection module 3-1, an attitude detection module 3-2, and a temperature detection module 3-3. The vibration detection module 3-1 is fixedly mounted on the multi-view structured light sensor module 1-1, forming a rigid connection, and is used for vibration compensation of the cross-sectional profile measurement unit. The attitude detection module 3-2 is fixedly mounted on the rigid crossbeam of the moving carrier and is used for angle compensation of the cross-sectional profile measurement unit. The temperature detection module 3-3 is fixedly mounted on the rigid crossbeam of the moving carrier and is used for temperature compensation of the cross-sectional profile measurement unit.

[0052] like Figure 2 As shown Figure 1 The multi-line structured light sensor module 1-1 consists of multiple horizontally arranged line structured light sensors P0, P1, P2 and P... nThe multi-view structured light sensor module 1-1 collects and transmits the data to the data processing unit 4 for further processing and analysis.

[0053] like Figure 3 The diagram shows a flowchart of a three-dimensional spatial deformation measurement system based on laser profile differential scanning, provided by an embodiment of the present invention.

[0054] like Figure 3 As shown, it includes the following steps:

[0055] Step S301: Based on the mapping relationship between the feature points of the laser plane and the camera image plane, obtain the equation of the laser plane in the camera coordinate system.

[0056] Step S302: Use the assembly and adjustment module 2-2 of the calibration unit to ensure the initial installation pose of each line structured light sensor; use the synchronization control module 2-1 of the calibration unit to control the synchronous data acquisition of each line structured light sensor; use the common toothed calibration target 2-3 of the calibration unit to obtain the rotation and translation matrix.

[0057] Specifically, such as Figure 4 The diagram illustrates how the multi-view structured light sensor module 1-1 achieves a global coordinate system using a common toothed calibration target 2-3. A toothed calibration target with specific geometric shape and dimensional accuracy is designed and fabricated, ensuring strict consistency in the spacing between its tooth tips and roots, and establishing a certain angle between adjacent teeth. A calibration platform is built to ensure the position of the toothed calibration target can be adjusted. The relationship between the coordinate system of the toothed calibration target and the coordinate systems of each line structured light sensor is determined based on feature matching, thereby achieving a global coordinate system.

[0058] Step S303: Convert the two-dimensional image coordinates of the pixels into three-dimensional coordinates of the spatial points. Combine the information from the velocity sensing module 1-2 and the ranging module 1-3 to stitch together the data point cloud of each image to obtain the point cloud data of each line structured light sensor in the global coordinate system.

[0059] Specifically, suppose a certain point on the road surface is at... It is constantly scanned by the laser plane of a certain structured light sensor, let its coordinates be... The equation of the laser plane in a structured optical sensor is: (in , , , (These are the coefficients of the plane equation), and the camera focal length in the line structured light sensor is... The length of a unit pixel is The width of a unit pixel is The center coordinates of the camera image coordinate system are The scaling factor is The rotation matrix of the line structured light sensor relative to the global coordinate system is: The translation matrix is Solving equation (1) yields the coordinates of the point in the global coordinate system. .

[0060] (1)

[0061] By combining the information from the velocity sensing module 1-2 and the ranging module 1-3, the point cloud data of each line structured light sensor can be obtained by aggregating all the measurement points at different times.

[0062] Step S304: The point cloud data is corrected using the data obtained by the vibration detection module 3-1, attitude detection module 3-2 and temperature detection module 3-3 of the error compensation unit.

[0063] Step S305: Based on the deformation cloud points of a cross section at different times, perform time-domain difference operation on the deformation point cloud data of the cross section to establish the dynamic deformation of the cross section as time changes, and obtain the deformation time history model.

[0064] Specifically, such as Figure 5 As shown, with This will be explained using a line structured light sensor as an example. Point cloud data obtained from a line structured light sensor is used to reconstruct a 3D road map. The point cloud set obtained by the line structured light sensor is For any point ,definition for The surface normal vector pointing inwards at the point. A Gaussian filter is used. and normal vector The convolution calculation yields gradient space :

[0065] (2)

[0066] in , for Translation transformation after flipping; Here are the coordinates of the center point of the Gaussian filter. Standard deviation, These are the coordinates of the point to be filtered.

[0067] To achieve better gradient space solutions for discrete samples, a partitioning approximation method is adopted. The space is divided into different surface areas .make for At one of the points, we have , express The location, for If the normal vector information is obtained at a certain point, then equation (2) can be transformed into:

[0068] (3)

[0069] Establish the Poisson equation Find the indicator function After extracting the isosurface, the process is complete. 3D reconstruction of scanning data from a line structured light sensor, assuming its surface equation is... Similarly, the reconstructed surface of other line-structured optical sensors can be obtained. .

[0070] Next, construct the equation for the difference surface. Using the timestamp as a reference, , , ... Align the surfaces constructed at the specified locations to construct difference surfaces. Then there is

[0071] (4)

[0072] Each point in the difference equation represents the relative deformation at different times and locations. Since each measurement point is associated with time information, the dynamic deformation of the cross-section over time can be obtained by combining the difference equation. The three-dimensional distribution of the relative deformation in space can also be obtained from the difference surface equation.

[0073] Step S306: Using three-dimensional dynamics theory and pavement elasticity characteristics, combined with deformation time history model, the maximum value of micro-deformation in three-dimensional space is optimized.

[0074] Specifically, based on three-dimensional dynamic simulation, the three-dimensional dynamic problem is transformed into a plane strain problem with multiple discrete wavenumbers using time-frequency analysis. The road cross section is meshed, and the elastic properties of different base layers of the road are established based on elastic and viscoelastic constitutive equations. A three-dimensional deformation surface model is constructed, and the parameters of the three-dimensional deformation surface are optimally estimated by combining the difference surface constructed in step 305, thereby achieving global optimization for maximum deformation.

[0075] In summary, the embodiments of this application provide a high dynamic deformation measurement system and method based on laser cross-sectional profile differential scanning. The measurement system includes a cross-sectional profile measurement unit, a calibration unit, an error compensation unit, and a data processing unit. The cross-sectional profile measurement unit includes a multi-view structured light sensor module, a velocity sensing module, and a ranging module. The calibration unit includes a synchronization control module, an assembly and adjustment module, and a common toothed calibration target. The error compensation unit includes a vibration detection module, an attitude detection module, and a temperature detection module. The data processing unit is a device with programming and calculation functions. Secondly, the measurement method provided in this embodiment mainly includes six steps: Step 1: Based on the mapping relationship between the feature points of the laser plane and the camera image plane, obtain the equation of the laser plane in the camera coordinate system; Step 2: Use the calibration unit's assembly and adjustment module to ensure the initial installation pose of each line structured light sensor; use the calibration unit's synchronization control module to control the synchronous data acquisition of each line structured light sensor; use the common toothed calibration target of the calibration unit to obtain the rotation and translation matrix; Step 3: Convert the two-dimensional image coordinates of the pixels into three-dimensional coordinates of the spatial points, and combine the velocity sensing module and the ranging module... Step 1: Using the information of the blocks, the point cloud data of each image is stitched together to obtain the point cloud data of each line structured light sensor in the global coordinate system; Step 2: Using the data obtained by the vibration detection module, attitude detection module and temperature detection module of the error compensation unit, the point cloud data is corrected; Step 3: Based on the deformation cloud points of a cross section at different times, the cross section deformation point cloud data is subjected to time-domain difference operation to establish the dynamic deformation of the cross section with time, and the deformation time history model is obtained; Step 4: Through three-dimensional dynamics theory and road elasticity characteristics, combined with the deformation time history model, the maximum value of micro-deformation in three-dimensional space is optimized.

[0076] In the above steps, the influence of environmental factors such as vibration, attitude and temperature on the measurement results was fully considered, which improved the accuracy of the original three-dimensional point cloud data. Secondly, in the three-dimensional reconstruction process, the concept of differential surfaces was adopted to break through the expression of micro-deformation in three-dimensional space. Finally, the three-dimensional dynamic model and the constitutive equation of road surface characteristics were combined to make the theoretical basis for parameter estimation of differential surfaces more sufficient and the global optimization results more accurate.

[0077] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A measurement method for a high dynamic deformation measurement system based on laser cross-sectional profile differential scanning, characterized in that, The high dynamic deformation measurement system based on laser cross-sectional profile differential scanning includes a cross-sectional profile measurement unit, a calibration unit, an error compensation unit, and a data processing unit. The cross-sectional profile measurement unit includes a multi-view structured light sensor module, a velocity sensing module, and a ranging module. The calibration unit includes a synchronization control module, an assembly and adjustment module, and a common toothed calibration target. The error compensation unit includes a vibration detection module, an attitude detection module, and a temperature detection module. The data processing unit is a device with programming and calculation functions. The measurement method includes the following steps: (1) Calibration of laser plane and image plane: Based on the mapping relationship of feature points of laser plane on camera image plane, the equation of laser plane in camera coordinate system is obtained, that is, the relative relationship between laser plane and camera coordinate system in a single line structured light sensor is determined; (2) Global Coordinate System 1: Determine the relative positions of multiple line structured light sensors, define the coordinate system of any one of the line structured light sensors as the global coordinate system, and describe the relative positions of the coordinate systems of the remaining line structured light sensors with the global coordinate system using rotation and translation matrices; before Global Coordinate System 1, use the calibration module of the calibration unit to ensure the initial installation pose of each line structured light sensor; use the synchronization control module of the calibration unit to control the synchronous data acquisition of each line structured light sensor; use the common toothed calibration target of the calibration unit to obtain the rotation and translation matrices; (3) Point cloud data acquisition: After the above steps (1) and (2), the two-dimensional image coordinates of the pixel points are converted into three-dimensional coordinates of the spatial points. Combined with the information of the velocity sensing module and the ranging module, the data point cloud of each image is stitched together to obtain the point cloud data of each line structure light sensor in the global coordinate system. (4) Error compensation: The point cloud data in step (3) is corrected using the data obtained by the vibration detection module, attitude detection module and temperature detection module of the above error compensation unit. (5) Deformation time history model construction: Based on the deformation cloud points of a cross section at different times, perform time domain difference operation on the deformation point cloud data of the cross section to establish the dynamic deformation of the cross section with time. (6) Global optimization of maximum deformation: Based on the three-dimensional dynamics theory and the elastic properties of the road surface, combined with the deformation time history model obtained in step (5), the maximum value of micro-deformation in the three-dimensional space is optimized.

2. The measurement method according to claim 1, characterized in that, The multi-view structured light sensor module is fixedly installed on the rigid crossbeam of the moving carrier. It consists of several line structured light sensors arranged in a longitudinal and transverse manner, with no less than three line structured light sensors. Each line structured light sensor independently acquires the cross-sectional profile perpendicular to the road surface. The speed sensing module is used to measure the speed of the moving carrier. The distance measuring module is used to measure the displacement of the moving carrier.

3. The measurement method according to claim 1, characterized in that, The synchronization control module is based on multi-source sensor synchronization control technology to realize synchronous acquisition of multi-source sensor data; the assembly and adjustment module is an assembly and adjustment tool used for precise installation of the cross-section profile measurement unit; the common toothed calibration target is a special calibration target designed for calibrating the mutual positional relationship between the sensors in the multi-view structured light sensor module of the cross-section profile measurement unit.

4. The measurement method according to claim 1, characterized in that, The vibration detection module is fixedly mounted on the multi-view structured light sensor module, forming a rigid connection, and is used for vibration compensation of the cross-sectional profile measurement unit; the attitude detection module is fixedly mounted on the rigid crossbeam of the moving carrier, and is used for angle compensation of the cross-sectional profile measurement unit; the temperature detection module is fixedly mounted on the rigid crossbeam of the moving carrier, and is used for temperature compensation of the cross-sectional profile measurement unit.

5. The measurement method according to claim 1, characterized in that, The specific steps (2) are as follows: design and process a toothed calibration target with a specific geometric shape and dimensional accuracy, with the tooth tip and tooth root spacing kept strictly consistent and a certain angle formed between adjacent teeth; build a calibration platform to ensure that the position of the toothed calibration target can be adjusted; determine the relationship between the coordinate system of the toothed calibration target and the coordinate system of each line structured light sensor according to feature matching, and realize the global coordinate system one.

6. The measurement method according to claim 1, characterized in that, The specific steps (3) are as follows: Suppose a point on the road surface is located at... It is constantly scanned by the laser plane of a certain structured light sensor, let its coordinates be... The equation of the laser plane in a structured optical sensor is: ,in , , , The coefficients are those of the plane equation, and the focal length of the camera in the line structured light sensor is... The length of a unit pixel is The width of a unit pixel is The center coordinates of the camera image coordinate system are The scaling factor is The rotation matrix of the line structured light sensor relative to the global coordinate system is: The translation matrix is Solving equation (1) yields the coordinates of the point in the global coordinate system. ; （1） By combining the information from the velocity sensing module and the ranging module, all measurement points at different times are aggregated to obtain point cloud data of each line structured light sensor.

7. The measurement method according to claim 1, characterized in that, The specific steps (5) are as follows: According to Point cloud data obtained by a line structured light sensor is used to reconstruct the three-dimensional roadmap; The point cloud set obtained by the line structured light sensor is For any point ,definition for The surface normal vector pointing inwards at the point; a Gaussian filter is used. and normal vector The convolution calculation yields gradient space : （2） in , for Translation transformation after flipping; The coordinates of the center point of the Gaussian filter are: Standard deviation, The coordinates of the point to be filtered; To achieve better gradient space solutions for discrete samples, a partitioning approximation method is adopted. The space is divided into different surface areas ;make for At one of the points, we have , express The location, for If the normal vector information is obtained at a certain point, then equation (2) can be transformed into: (3) Establish the Poisson equation Find the indicator function After extracting the isosurface, the process is complete. 3D reconstruction of scanning data from a line structured light sensor, assuming its surface equation is... Similarly, the reconstructed surfaces of other line structured light sensors can be obtained. ; Next, construct the equation of the difference surface; Based on timestamp , , ... Align the surfaces constructed at the specified locations to construct difference surfaces. Then there is (4) Each point in the difference equation represents the relative deformation at different times and locations. Since each measurement point is associated with time information, the dynamic deformation of the cross section over time can be obtained by combining the difference equation, and the three-dimensional distribution of the relative deformation in space can also be obtained from the difference surface equation.

8. The measurement method according to claim 1, characterized in that, The specific steps (6) are as follows: Based on three-dimensional dynamic simulation, the three-dimensional dynamic problem is transformed into a plane strain problem with multiple discrete wavenumbers using time-frequency analysis method. The cross section of the road is meshed. Based on the elastic and viscoelastic constitutive equations, the elastic properties of different base layers of the road are established respectively. A three-dimensional deformation surface model is constructed. Combined with the differential surface constructed in step (5), the parameters of the three-dimensional deformation surface are optimally estimated to achieve global optimization of the maximum deformation.

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