Bridge floor smoothness variable control method and system based on point cloud data processing

Abstract

The invention is suitable for the technical field of bridge floor construction, and provides a bridge floor smoothness variable control method and system based on point cloud data processing, and the method comprises the following steps: obtaining road surface point cloud data through a vehicle-mounted road surface scanning module integrated with a laser radar, a first GNSS receiver and an inertial measurement unit IMU; preprocessing the road surface point cloud data, fusing the GNSS trajectory and the IMU attitude data to generate a continuous three-dimensional trajectory, and generating an original road surface model based on the road surface point cloud data; an NSGA-II genetic algorithm is adopted, the tangent length and the longitudinal slope change rate serve as variables, a multi-objective optimization function is constructed, and the optimal milling thickness is determined; and carrying out automatic adjustment on the finish milling depth through a milling machine carrying a second GNSS receiver and a GX-60 control center. According to the invention, based on the pre-scanned pavement condition, accurate control of the bridge floor finish-milling planing flatness is realized in an intelligent dynamic control mode, and the influence of human errors is avoided.

Description

Technical Field

[0001] This invention relates to the field of bridge deck construction technology, specifically to a bridge deck smoothness variable control method and system based on point cloud data processing. Background Technology

[0002] In modern transportation construction, bridge engineering, as a critical infrastructure, directly affects the smoothness and safety of road traffic. Bridge deck construction, as a crucial component of bridge engineering, is closely related to its quality, service life, and driving comfort. With the continuous development of bridge engineering and the increasing demands for construction, bridge deck smoothness and durability have become important quality evaluation indicators. However, traditional precision milling processes primarily rely on manual operation of machinery for adjustment and control. This method demands a high level of experience from operators and is often subject to many uncertainties due to subjective human intervention, easily leading to insufficient precision and uneven surfaces, thus affecting the overall quality of bridge deck construction. Therefore, a bridge deck smoothness variable control method and system based on point cloud data processing is needed to address these issues. Summary of the Invention

[0003] In view of the shortcomings of the existing technology, the purpose of this invention is to provide a bridge deck smoothness variable control method and system based on point cloud data processing, so as to solve the problems existing in the above-mentioned background technology.

[0004] This invention is implemented as follows: a bridge deck smoothness variable control method based on point cloud data processing, the method comprising the following steps: Road surface point cloud data is acquired by an onboard road surface scanning module that integrates a lidar, a first GNSS receiver, and an inertial measurement unit (IMU). The onboard road surface scanning module is installed on a vehicle for use. The road surface point cloud data is preprocessed, GNSS trajectory and IMU attitude data are fused to generate continuous three-dimensional trajectory lines, and the original road surface model is generated based on the road surface point cloud data to extract road surface distress features. The constraints are determined, including flatness, milling depth and material waste. The NSGA-II genetic algorithm is used to construct a multi-objective optimization function with tangent length and longitudinal slope change rate as variables, determine the optimal milling thickness, and obtain the optimal design 3D model. The milling depth is automatically adjusted by a milling machine equipped with a second GNSS receiver and a GX-60 control center.

[0005] As a further aspect of the present invention: the vehicle speed is controlled at 10-30 km / h, and a variable spacing scanning strategy is adopted; spatial reference calibration is performed before the vehicle travels, and the coordinate transformation relationship between the lidar and GNSS is established by using a SLAM algorithm through several preset control points in the calibration field; multi-sensor time synchronization is implemented, and a pulse second signal triggering mechanism is adopted to ensure that the time error between point cloud data and GNSS positioning data is ≤10ms.

[0006] As a further aspect of the present invention: the step of preprocessing the road surface point cloud data specifically includes: The RANSAC algorithm is used to remove outliers caused by vehicle bumps, and IMU data interpolation is used to compensate for GNSS signal loss areas. Morphological filtering was applied to the drainage ditches and the edges of the crash barriers on the bridge deck to preserve structural features; An iterative nearest-point algorithm is used to stitch together multiple scan segments to reduce registration errors.

[0007] As a further aspect of the present invention: the step of generating an original pavement model based on pavement point cloud data and extracting pavement distress features specifically includes: The road surface point cloud data is projected onto a horizontal plane, and a regular grid DEM is generated using thin plate spline interpolation. Kriging interpolation was used to fill in the missing data regions, and a Gaussian model was selected as the interpolation kernel function. By fusing RGB data collected by a panoramic camera, a color point cloud is generated, and texture mapping is performed on the DEM; Conduct rut detection, crack detection, and structural misalignment detection to determine the characteristics of pavement defects.

[0008] As a further aspect of this invention: the step of using the NSGA-II genetic algorithm, with tangent length and longitudinal slope change rate as variables, to construct a multi-objective optimization function and determine the optimal milling thickness specifically includes: Determine the range of tangent length and dispersion step size, and construct a multi-objective optimization function based on constraints; Configure the NSGA-II genetic algorithm, initialize the population, perform genetic operations and fitness evaluation; The optimization process involves iterative steps. When the maximum number of iterations is reached, the optimal solution is selected using the TOPSIS method to determine the best milling thickness.

[0009] As a further aspect of the present invention: the method also includes intelligent estimation of material usage, the specific steps of which are as follows: Boolean operations are performed between the optimal design 3D model and the original BIM model to generate a voxel mesh for the milled area. Volume statistics were performed for different material types separately; Input the gradation requirements of the recycled material, generate the particle size distribution curve of the milled material through Monte Carlo simulation, and output it to the control system of the mixing plant.

[0010] Another object of the present invention is to provide a bridge deck smoothness variable control system based on point cloud data processing, the system comprising: The vehicle-mounted road surface scanning module is used to acquire road surface point cloud data. It integrates a lidar, a first GNSS receiver, and an inertial measurement unit (IMU). The vehicle-mounted road surface scanning module is installed on a vehicle for use. The point cloud data processing module is used to preprocess the road surface point cloud data, fuse GNSS trajectory and IMU attitude data, generate continuous three-dimensional trajectory lines, generate the original road surface model based on the road surface point cloud data, and extract road surface distress features. The optimal milling thickness module is used to determine the constraints, including flatness, milling depth, and material waste. The NSGA-II genetic algorithm is used to construct a multi-objective optimization function with tangent length and longitudinal slope change rate as variables to determine the optimal milling thickness and obtain the optimal design 3D model. The milling depth adjustment module is used to automatically adjust the fine milling depth of a milling machine equipped with a second GNSS receiver and a GX-60 control center.

[0011] As a further aspect of the present invention: the point cloud data processing module includes: The gross error removal unit is used to remove outliers caused by vehicle bumps using the RANSAC algorithm and to compensate for GNSS signal loss areas using IMU data interpolation. A fine noise reduction unit is used to apply morphological filtering to the edges of the bridge deck drainage ditch and crash barrier to preserve structural features; The data registration unit is used to stitch together multiple scan segments using the iterative nearest point algorithm to reduce registration errors.

[0012] As a further aspect of the present invention: the point cloud data processing module further includes: The grid DEM generation unit is used to project road point cloud data onto a horizontal plane and generates a regular grid DEM using thin plate spline interpolation. The missing data processing unit is used to fill missing data regions using Kriging interpolation, with the interpolation kernel function selected as a Gaussian model. The texture mapping enhancement unit is used to fuse RGB data acquired by the panoramic camera to generate a color point cloud and perform texture mapping on the DEM; The pavement distress feature unit is used for rutting detection, crack detection, and structural misalignment detection to determine pavement distress features.

[0013] As a further aspect of the present invention: the optimal milling thickness module includes: Variable design constraint units are used to determine the range of tangent length and dispersion size, and a multi-objective optimization function is constructed based on the constraints. The genetic algorithm configuration unit is used to configure the NSGA-II genetic algorithm, initialize the population, perform genetic operations, and evaluate fitness. The optimization iteration control unit is used to perform optimization iterations. When the maximum number of iterations is reached, the optimal solution is selected using the TOPSIS method to determine the best milling thickness.

[0014] Compared with the prior art, the beneficial effects of the present invention are: This invention utilizes a vehicle-mounted scanning module composed of a lidar, GNSS receiver, and IMU to acquire high-density road surface point cloud data in real time. Combined with GNSS trajectory and IMU attitude fusion technology, it generates continuous 3D trajectory lines and an original road surface model. This accurately recreates the microscopic morphology of the bridge deck, eliminating human measurement errors and providing a millimeter-level benchmark for subsequent milling. Employing the NSGA-II genetic algorithm, with smoothness, milling depth, and material waste as constraints, the optimal milling thickness is determined through dynamic optimization of tangent length and longitudinal slope change rate. This algorithm ensures the longitudinal smoothness of the bridge deck while avoiding local over-milling or under-milling, achieving a globally optimal solution and significantly improving construction accuracy. The optimal 3D design model is used, and the milling machine equipped with a GX-60 control center reads the data in real time, dynamically adjusting the milling depth. The GX-60, as a highly reliable industrial controller, ensures the stability and response speed of command execution, completely eliminating the uncertainty of human operation. Attached Figure Description

[0015] Figure 1 This is a flowchart of a bridge deck smoothness variable control method based on point cloud data processing.

[0016] Figure 2 This is a flowchart illustrating the preprocessing of road surface point cloud data in a bridge deck smoothness variable control method based on point cloud data processing.

[0017] Figure 3 This is a flowchart illustrating the generation of the original road surface model in a bridge deck smoothness variable control method based on point cloud data processing.

[0018] Figure 4 This is a flowchart of intelligent estimation of material usage in a bridge deck smoothness variable control method based on point cloud data processing.

[0019] Figure 5 This is a schematic diagram of a bridge deck smoothness variable control system based on point cloud data processing. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0021] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0022] like Figure 1 As shown, this embodiment of the invention provides a bridge deck smoothness variable control method based on point cloud data processing. The method includes the following steps: S100 acquires road point cloud data through an on-board road scanning module that integrates a lidar, a first GNSS receiver, and an inertial measurement unit (IMU). The on-board road scanning module is installed on a vehicle for use. S200 preprocesses road surface point cloud data, merges GNSS trajectory and IMU attitude data to generate continuous three-dimensional trajectory lines, generates original road surface model based on road surface point cloud data, and extracts road surface distress features. S300, determine the constraints, which include flatness, milling depth and material waste. Use the NSGA-II genetic algorithm to construct a multi-objective optimization function with tangent length and longitudinal slope change rate as variables, determine the optimal milling thickness, and obtain the optimal design 3D model. The S400 uses a milling machine equipped with a second GNSS receiver and a GX-60 control center to automatically adjust the fine milling depth.

[0023] like Figure 2 As shown, in a preferred embodiment of the present invention, the step of preprocessing the road surface point cloud data specifically includes: S201 uses the RANSAC algorithm to remove outliers caused by vehicle bumps and uses IMU data interpolation to compensate for GNSS signal loss areas. S202, morphological filtering is applied to the bridge deck drainage ditch and the edge of the crash barrier to preserve structural features; S203 uses an iterative nearest-point algorithm to stitch together multiple scan segments to reduce registration errors.

[0024] Specifically, an iterative nearest-point algorithm is used to stitch together multiple scan segments to reduce registration errors. The specific steps are as follows: Based on the bridge deck drainage ditch and the edge of the crash barrier, morphological filtering is applied to obtain the geometric features of the drainage ditch point cloud and the spatial gradient features of the reflectivity of the crash barrier edge; based on the geometric features of the drainage ditch point cloud, the covariance matrix of the normal vector and curvature is calculated, and the three-dimensional noise-resistant feature encoding is obtained using the covariance matrix. Using the material reflection feature parameters of the anti-collision edge as constraints, three-dimensional noise-resistant feature encoding is used for verification to obtain a subset of structural feature descriptions. The subset of structural feature descriptions is used to establish a bidirectional matching channel between adjacent scan segments. The forward matching channel uses the anti-collision wall features of the reference segment as anchor points, and the reverse matching channel uses the direction of the drainage ditch as a constraint condition. When the bidirectional matching result fails to be verified in the topology rule base, local feature reorganization is performed to remove matching pairs that violate the physical connection commonality of the drainage system. The preliminary matching point cloud pairs contain the correspondence between the anti-collision wall and drainage ditch features. Using preliminary matched point cloud pairs, a global affine transformation model is constructed with the longitudinal slope tolerance of the drainage ditch as a rigid constraint. The Levenberg-Marquardt algorithm is used to optimize the rotation and translation parameters in the global affine transformation model, and a coarse matching point cloud is generated. The coarse matching point cloud includes a confidence score. The search radius of the ICP algorithm is adjusted based on the confidence score, and the optimal registration parameters are selected through the Pareto front solution set to obtain the accurate registration point cloud, which includes the error distribution matrix. Based on the regional error magnitude in the error distribution matrix, a Gaussian process regression model is constructed to predict the affected area of ​​the splicing joint. Within the predicted affected area of ​​the splicing joint, gradient-preserving Poisson fusion is performed on the elevation parameters in the road surface point cloud data, and topological consistency checks are performed on the drainage ditch connections to obtain a spatially continuous road surface point cloud model. The spatially continuous road surface point cloud model is used to reduce registration errors.

[0025] Furthermore, this invention forms a closed-loop optimization system from coarse registration to fine registration to elastic compensation by incorporating structural feature constraints throughout the entire registration process.

[0026] In this embodiment of the invention, the road surface point cloud data needs to be preprocessed. First, the RANSAC algorithm is used to remove outliers caused by vehicle bumps, with a threshold of ±5cm. Then, IMU data interpolation is used to compensate for GNSS signal loss areas (such as tunnel entrances). After compensation, the trajectory error is ≤10cm.

[0027] like Figure 3 As shown in the preferred embodiment of the present invention, the step of generating an original pavement model based on pavement point cloud data and extracting pavement distress features specifically includes: S204: Project the road surface point cloud data onto the horizontal plane and use thin plate spline interpolation to generate a regular grid DEM; S205, Kriging interpolation is used to fill missing data regions, and Gaussian model is selected as the interpolation kernel function; S206 integrates RGB data collected by a panoramic camera to generate a color point cloud and performs texture mapping on the DEM; S207, conduct rut detection, crack detection, and structural misalignment detection to determine the characteristics of pavement defects.

[0028] Specifically, rut detection, crack detection, and structural misalignment detection are conducted to determine the characteristics of pavement defects. The specific steps are as follows: Texture mapping is performed on a DEM to obtain the texture mapping relationship of a color DEM. Regular grid elevation data is generated from a regular grid DEM. The texture mapping relationship of the color DEM and the regular grid elevation data are fused to obtain a coupled refined DEM model. Opening and closing operations are performed on the coupled data in the coupled refined DEM model to obtain a multimodal fused DEM field. Based on the elevation gradient field and texture feature matrix in the multimodal fused DEM field, a spatiotemporal correlation feature map is generated. The spatiotemporal correlation feature map is used as a three-dimensional defect feature spatial distribution map. The three-dimensional defect feature spatial distribution map includes material labels, construction stage markers, and material reflectivity classification data. Based on the regional confidence index in Kriging interpolation and combined with material labels, an initial detection parameter template is obtained. The initial detection parameter template is then used to analyze construction stage markers, and combined with spatiotemporal correlation features, a time decay function is established along the driving direction. Based on the time decay function and according to the range of the variogram in Kriging interpolation, a dynamic detection window parameter set is obtained. Using the dynamic detection window parameter set and material reflectivity classification data, a material reflectivity-disease morphology mapping relationship is established. Using the material reflectivity-disease morphology mapping relationship and the residual analysis results in Kriging interpolation, a detection parameter set is obtained. This detection parameter set includes a disease confidence threshold. The detection parameter set is adjusted using error parameters from the multi-scan segment stitching process, and a bidirectional elastic matching model is established on both sides of the crack. The cross-modal attention layer of the bidirectional elastic matching model is weighted based on the detection parameter set and the disease confidence threshold to obtain an optimized bidirectional elastic matching model. A multi-dimensional disease feature vector set is obtained through the optimized bidirectional elastic matching model. An IDW model is constructed based on a multi-dimensional feature vector set of defects and preserved structural features; a regional defect weight map is generated based on the IDW model and combined with rut features to obtain a defect classification decision tree; the defect classification decision tree is used to determine the characteristics of road defects.

[0029] Furthermore, this invention significantly improves the detection reliability of complex road surface scenarios through multi-dimensional data fusion and deep embedding of engineering physical constraints.

[0030] In this embodiment of the invention, the point cloud is projected onto a horizontal plane, and a regular grid DEM is generated using thin-plate spline interpolation with a grid resolution of 1cm × 1cm. Then, missing data areas (such as areas covered by water accumulation) are filled using Kriging interpolation with a Gaussian kernel function. Next, RGB data collected by a panoramic camera is fused to generate a color point cloud, and texture mapping is performed on the DEM to generate a realistic road surface visualization model.

[0031] As a preferred embodiment of the present invention, the step of using the NSGA-II genetic algorithm to construct a multi-objective optimization function with tangent length and longitudinal slope change rate as variables to determine the optimal milling thickness specifically includes: S301, determine the range of tangent length and dispersion step size, and construct a multi-objective optimization function based on the constraints; S302, configure the NSGA-II genetic algorithm, initialize the population, perform genetic operations and fitness evaluation; S303 is optimized iteratively. When the maximum number of iterations is reached, the optimal solution is selected using the TOPSIS method to determine the best milling thickness.

[0032] Specifically, the range of tangent length and dispersion step size are determined, and a multi-objective optimization function is constructed based on the constraints. The specific steps are as follows: Given the allowable threshold for longitudinal slope change rate, the bridge structural strength verification results and thickness mutation penalty factor are obtained from the structural verification report; the total allowable milling thickness is determined through the bridge structural strength verification results; and the exponential decay coefficient is determined through the original bridge deck scanning data. A bridge deck point cloud model for detecting road surface defects is constructed by using road surface defect features; the bridge deck is then divided into sections using the bridge deck point cloud model for detecting defects, and a list of original tangent lengths and longitudinal slope change rates for each section is obtained. Candidate schemes are generated by using the range of tangent length and the allowable threshold of longitudinal slope change rate, with the step length as the increment. A list of tangent lengths for each segment is obtained through candidate schemes; based on the list of tangent lengths for each segment and the original list of tangent lengths for each segment, the tangent length adjustment amount is obtained; the ratio of the tangent length adjustment amount to the range of tangent lengths is calculated to obtain the tangent length standardization ratio; Given the longitudinal slope design value, the adjusted longitudinal slope value is obtained through candidate schemes; the absolute difference between the adjusted longitudinal slope value and the longitudinal slope design value is obtained by combining the adjusted longitudinal slope value and the longitudinal slope change rate list; the absolute difference between the adjusted longitudinal slope value and the longitudinal slope design value is then substituted into the exponential function for nonlinear mapping to obtain the longitudinal slope deviation influence factor; where the exponential decay coefficient is used as the base of the exponential term of the exponential function. The square of the standardized ratio of the tangent length for each segment is combined with the corresponding longitudinal slope deviation influence factor, and then summed to obtain the first objective term; The absolute difference between adjacent milling thicknesses is obtained by measuring the milling thickness of each section. The absolute differences between all adjacent milling thicknesses are accumulated to obtain the cumulative thickness mutation value. The total allowable milling thickness is normalized to the cumulative thickness mutation value to calculate the over-limit rate. Then, the thickness mutation penalty factor is combined to obtain the thickness mutation penalty amount. For each section, the absolute difference between the adjusted longitudinal slope value and the longitudinal slope design value is divided by the allowable threshold for the longitudinal slope change rate, and then summed to obtain the summation result; the summation result is added to the thickness abrupt change penalty to obtain the second objective term; Candidate solutions are screened using the first and second objective terms to obtain compliant solutions; non-dominated sorting is performed on the compliant solutions to generate a Pareto optimal solution set; and a multi-objective optimization function is constructed using the Pareto optimal solution set.

[0033] Furthermore, this invention overcomes the technical contradiction of "smoothness-stability" in the traditional bridge deck milling thickness design by using a triple mechanism of standardized constraint quantification, multi-objective collaborative optimization, and engineering experience algorithmization, providing a replicable and verifiable intelligent decision-making paradigm for road maintenance engineering.

[0034] like Figure 4 As shown in the preferred embodiment of the present invention, the method further includes intelligent estimation of material usage, specifically the following steps: S501 performs Boolean operations on the optimal design 3D model and the original BIM model to generate a voxel mesh for the milled area. S502, statistical volume analysis is performed for different material types; S503: Input the gradation requirements of recycled materials, generate the particle size distribution curve of milled material through Monte Carlo simulation, and output it to the mixing plant control system.

[0035] Specifically, the process involves inputting the gradation requirements of the recycled material, generating a milled material particle size distribution curve through Monte Carlo simulation, and then outputting it to the mixing plant control system. The specific steps are as follows: By statistically analyzing different material types, we obtain the total volume of milled material, the threshold of the gradation range of recycled aggregate, and the actual material inventory data of the mixing plant; we establish a dynamic adjustment model for gradation parameters, and based on the current total volume of milled material and the actual material inventory data of the mixing plant, we dynamically form acceptable gradation range constraints and obtain the gradation optimization parameter matrix. The spatial distribution characteristics of the voxel grid in the milled area are obtained by using the voxel grid in the milled area. Based on the spatial distribution characteristics of the voxel grid in the milled area and the gradation optimization parameter matrix, a three-dimensional probability density function is established. Based on the three-dimensional probability density function, an adaptive Monte Carlo sampling strategy is adopted to increase sampling points in areas with high spatial density and reduce sampling points in areas with low density. At the same time, a Markov chain mechanism is introduced to make each sampling result correlated with the previous sampling result, thereby generating and obtaining the probability distribution field of milled material particle size. The probability distribution field of milled aggregate particle size is mapped to a two-dimensional gradation curve space, and the expected value and variance of the throughput of each particle size are calculated. Based on the expected value and variance of the throughput of each particle size, the threshold of the gradation interval of recycled aggregate is used as the boundary condition. The membership function of the gradation conformity is established by using the fuzzy comprehensive evaluation method. The simulation results are evaluated in multiple dimensions to obtain the set of optimized gradation curve schemes. Obtain the operating parameters of the batching plant equipment; use the dynamic adjustment model of gradation parameters to correct the set of optimized gradation curve schemes, and construct a matching degree evaluation model with the corrected optimized gradation curve schemes and the operating parameters of the batching plant equipment; use the matching degree evaluation model to comprehensively consider the operating conditions of the mixing machine, screen the gradation schemes with the equipment compliance rate in the optimal range, and generate a set of gradation control instructions; input the set of gradation control instructions into the batching plant control system.

[0036] Furthermore, this invention achieves a balanced improvement in the three dimensions of resource utilization, equipment efficiency, and product quality by modeling the spatial distribution characteristics of milled materials and dynamically coupling and optimizing the equipment operating conditions.

[0037] like Figure 5 As shown, this embodiment of the invention also provides a bridge deck smoothness variable control system based on point cloud data processing, the system comprising: The vehicle-mounted road surface scanning module 100 is used to acquire road surface point cloud data. It integrates a lidar, a first GNSS receiver, and an inertial measurement unit (IMU). The vehicle-mounted road surface scanning module 100 is installed on a vehicle for use. The point cloud data processing module 200 is used to preprocess the road surface point cloud data, fuse GNSS trajectory and IMU attitude data, generate continuous three-dimensional trajectory lines, generate the original road surface model based on the road surface point cloud data, and extract road surface distress features. The optimal milling thickness module 300 is used to determine the constraints, including flatness, milling depth and material waste. The NSGA-II genetic algorithm is used to construct a multi-objective optimization function with tangent length and longitudinal slope change rate as variables to determine the optimal milling thickness and obtain the optimal design 3D model. The milling depth adjustment module 400 is used for the automated adjustment of the fine milling depth of a milling machine equipped with a second GNSS receiver and a GX-60 control center.

[0038] In a preferred embodiment of the present invention, the point cloud data processing module 200 includes: The gross error removal unit is used to remove outliers caused by vehicle bumps using the RANSAC algorithm and to compensate for GNSS signal loss areas using IMU data interpolation. A fine noise reduction unit is used to apply morphological filtering to the edges of the bridge deck drainage ditch and crash barrier to preserve structural features; The data registration unit is used to stitch together multiple scan segments using the iterative nearest point algorithm to reduce registration errors.

[0039] In a preferred embodiment of the present invention, the point cloud data processing module 200 further includes: The grid DEM generation unit is used to project road point cloud data onto a horizontal plane and generates a regular grid DEM using thin plate spline interpolation. The missing data processing unit is used to fill missing data regions using Kriging interpolation, with the interpolation kernel function selected as a Gaussian model. The texture mapping enhancement unit is used to fuse RGB data acquired by the panoramic camera to generate a color point cloud and perform texture mapping on the DEM; The pavement distress feature unit is used for rutting detection, crack detection, and structural misalignment detection to determine pavement distress features.

[0040] In a preferred embodiment of the present invention, the optimal milling thickness module 300 includes: Variable design constraint units are used to determine the range of tangent length and dispersion size, and a multi-objective optimization function is constructed based on the constraints. The genetic algorithm configuration unit is used to configure the NSGA-II genetic algorithm, initialize the population, perform genetic operations, and evaluate fitness. The optimization iteration control unit is used to perform optimization iterations. When the maximum number of iterations is reached, the optimal solution is selected using the TOPSIS method to determine the best milling thickness.

[0041] The above description only details the preferred embodiments of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0042] It should be understood that although the steps in the flowcharts of the various embodiments of the present invention are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the various embodiments may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0043] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0044] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the disclosure in the specification and embodiments. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the claims.

Claims

1. A bridge deck smoothness variable control method based on point cloud data processing, characterized in that, The method includes the following steps: Road surface point cloud data is acquired by an onboard road surface scanning module that integrates a lidar, a first GNSS receiver, and an inertial measurement unit (IMU). The onboard road surface scanning module is installed on a vehicle for use. The road surface point cloud data is preprocessed, GNSS trajectory and IMU attitude data are fused to generate continuous three-dimensional trajectory lines, and the original road surface model is generated based on the road surface point cloud data to extract road surface distress features. The constraints are determined, including flatness, milling depth and material waste. The NSGA-II genetic algorithm is used to construct a multi-objective optimization function with tangent length and longitudinal slope change rate as variables, determine the optimal milling thickness, and obtain the optimal design 3D model. The milling depth is automatically adjusted by a milling machine equipped with a second GNSS receiver and a GX-60 control center.

2. The bridge deck smoothness variable control method based on point cloud data processing according to claim 1, characterized in that, The steps for preprocessing the road surface point cloud data specifically include: The RANSAC algorithm is used to remove outliers caused by vehicle bumps, and IMU data interpolation is used to compensate for GNSS signal loss areas. Morphological filtering was applied to the drainage ditches and the edges of the crash barriers on the bridge deck to preserve structural features; An iterative nearest-point algorithm is used to stitch together multiple scan segments to reduce registration errors.

3. The bridge deck smoothness variable control method based on point cloud data processing according to claim 2, characterized in that, The iterative nearest-point algorithm is used to stitch together multiple scan segments to reduce registration errors. The specific steps include: Based on the bridge deck drainage ditch and the edge of the crash barrier, morphological filtering is applied to obtain the geometric features of the drainage ditch point cloud and the spatial gradient features of the reflectivity of the crash barrier edge; based on the geometric features of the drainage ditch point cloud, the covariance matrix of the normal vector and curvature is calculated, and the three-dimensional noise-resistant feature encoding is obtained using the covariance matrix. Using the material reflection feature parameters of the anti-collision edge as constraints, three-dimensional noise-resistant feature encoding is used for verification to obtain a subset of structural feature descriptions. The subset of structural feature descriptions is used to establish a bidirectional matching channel between adjacent scan segments to obtain preliminary matching point cloud pairs. The preliminary matching point cloud pairs contain the correspondence between the features of the anti-collision wall and the drainage ditch. Using preliminary matched point cloud pairs, a global affine transformation model is constructed with the longitudinal slope tolerance of the drainage ditch as a rigid constraint. The Levenberg-Marquardt algorithm is used to optimize the rotation and translation parameters in the global affine transformation model, and a coarse matching point cloud is generated. The coarse matching point cloud includes a confidence score. The search radius of the ICP algorithm is adjusted based on the confidence score, and the optimal registration parameters are selected through the Pareto front solution set to obtain the accurate registration point cloud; the accurate registration point cloud contains the error distribution matrix. Based on the regional error magnitude in the error distribution matrix, a Gaussian process regression model is constructed to predict the affected area of ​​the splicing joint. Within the predicted affected area of ​​the splicing joint, gradient-preserving Poisson fusion is performed on the elevation parameters in the road surface point cloud data, and topological consistency checks are performed on the drainage ditch connections to obtain a spatially continuous road surface point cloud model. The spatially continuous road surface point cloud model is used to reduce registration errors.

4. The bridge deck smoothness variable control method based on point cloud data processing according to claim 3, characterized in that, The steps of generating an original pavement model based on pavement point cloud data and extracting pavement distress features specifically include: The road surface point cloud data is projected onto a horizontal plane, and a regular grid DEM is generated using thin plate spline interpolation. Kriging interpolation was used to fill in the missing data regions, and a Gaussian model was selected as the interpolation kernel function. By fusing RGB data collected by a panoramic camera, a color point cloud is generated, and texture mapping is performed on the DEM; Conduct rut detection, crack detection, and structural misalignment detection to determine the characteristics of pavement defects.

5. The bridge deck smoothness variable control method based on point cloud data processing according to claim 4, characterized in that, The process of conducting rutting detection, crack detection, and structural misalignment detection to determine the characteristics of pavement defects includes the following steps: Texture mapping is performed on a DEM to obtain the texture mapping relationship of a color DEM. Regular grid elevation data is generated from a regular grid DEM. The texture mapping relationship of the color DEM and the regular grid elevation data are fused to obtain a coupled refined DEM model. Opening and closing operations are performed on the coupled data in the coupled refined DEM model to obtain a multimodal fused DEM field. Based on the elevation gradient field and texture feature matrix in the multimodal fused DEM field, a spatiotemporal correlation feature map is generated. The spatiotemporal correlation feature map is used as a three-dimensional defect feature spatial distribution map. The three-dimensional defect feature spatial distribution map includes material labels, construction stage markers, and material reflectivity classification data. Based on the regional confidence index in Kriging interpolation and combined with material labels, an initial detection parameter template is obtained. The initial detection parameter template is then used to analyze construction stage markers, and combined with spatiotemporal correlation features, a time decay function is established along the driving direction. Based on the time decay function and according to the range of the variogram in Kriging interpolation, a dynamic detection window parameter set is obtained. Using the dynamic detection window parameter set and material reflectivity classification data, a material reflectivity-disease morphology mapping relationship is established. Using the material reflectivity-disease morphology mapping relationship and the residual analysis results in Kriging interpolation, a detection parameter set is obtained. This detection parameter set includes a disease confidence threshold. The detection parameter set is adjusted using error parameters from the multi-scan segment stitching process, and a bidirectional elastic matching model is established on both sides of the crack. The cross-modal attention layer of the bidirectional elastic matching model is weighted based on the detection parameter set and the disease confidence threshold to obtain an optimized bidirectional elastic matching model. A multi-dimensional disease feature vector set is obtained through the optimized bidirectional elastic matching model. An IDW model is constructed based on a multi-dimensional feature vector set of defects and preserved structural features; a regional defect weight map is generated based on the IDW model and combined with rut features to obtain a defect classification decision tree; the defect classification decision tree is used to determine the characteristics of road defects.

6. The bridge deck smoothness variable control method based on point cloud data processing according to claim 5, characterized in that, The NSGA-II genetic algorithm is used to construct a multi-objective optimization function with tangent length and longitudinal slope change rate as variables. The steps to determine the optimal milling thickness include: Determine the range of tangent length and dispersion step size, and construct a multi-objective optimization function based on constraints; Configure the NSGA-II genetic algorithm, initialize the population, perform genetic operations and fitness evaluation; The optimization process involves iterative steps. When the maximum number of iterations is reached, the optimal solution is selected using the TOPSIS method to determine the best milling thickness.

7. The bridge deck smoothness variable control method based on point cloud data processing according to claim 6, characterized in that, Determine the range of tangent length and dispersion step size, and construct a multi-objective optimization function based on constraints. The specific steps are as follows: Given the allowable threshold for longitudinal slope change rate, the bridge structural strength verification results and thickness mutation penalty factor are obtained from the structural verification report; the total allowable milling thickness is determined through the bridge structural strength verification results; and the exponential decay coefficient is determined through the original bridge deck scanning data. A bridge deck point cloud model for detecting road surface defects is constructed by using road surface defect features; the bridge deck is then divided into sections using the bridge deck point cloud model for detecting defects, and a list of original tangent lengths and longitudinal slope change rates for each section is obtained. Candidate schemes are generated by using the range of tangent length and the allowable threshold of longitudinal slope change rate, with the step length as the increment. A list of tangent lengths for each segment is obtained through candidate schemes; based on the list of tangent lengths for each segment and the original list of tangent lengths for each segment, the tangent length adjustment amount is obtained; the ratio of the tangent length adjustment amount to the range of tangent lengths is calculated to obtain the tangent length standardization ratio; Given the longitudinal slope design value, the adjusted longitudinal slope value is obtained through candidate schemes; the absolute difference between the adjusted longitudinal slope value and the longitudinal slope design value is obtained by combining the adjusted longitudinal slope value and the longitudinal slope change rate list; the absolute difference between the adjusted longitudinal slope value and the longitudinal slope design value is then substituted into the exponential function for nonlinear mapping to obtain the longitudinal slope deviation influence factor; where the exponential decay coefficient is used as the base of the exponential term of the exponential function. The square of the standardized ratio of the tangent length for each segment is combined with the corresponding longitudinal slope deviation influence factor, and then summed to obtain the first objective term; The absolute difference between adjacent milling thicknesses is obtained by measuring the milling thickness of each section. The absolute differences between all adjacent milling thicknesses are accumulated to obtain the cumulative thickness mutation value. The total allowable milling thickness is normalized to the cumulative thickness mutation value to calculate the over-limit rate. Then, the thickness mutation penalty factor is combined to obtain the thickness mutation penalty amount. For each section, the absolute difference between the adjusted longitudinal slope value and the longitudinal slope design value is divided by the allowable threshold for the longitudinal slope change rate, and then summed to obtain the summation result; the summation result is added to the thickness abrupt change penalty to obtain the second objective term; Candidate solutions are screened using the first and second objective terms to obtain compliant solutions; non-dominated sorting is performed on the compliant solutions to generate a Pareto optimal solution set; and a multi-objective optimization function is constructed using the Pareto optimal solution set.

8. The bridge deck smoothness variable control method based on point cloud data processing according to claim 7, characterized in that, The method also includes intelligent estimation of material usage, the specific steps of which are as follows: Boolean operations are performed between the optimal design 3D model and the original BIM model to generate a voxel mesh for the milled area. Volume statistics were performed for different material types separately; Input the gradation requirements of the recycled material, generate the particle size distribution curve of the milled material through Monte Carlo simulation, and output it to the control system of the mixing plant.

9. The bridge deck smoothness variable control method based on point cloud data processing according to claim 8, characterized in that, Input the gradation requirements of the recycled material, generate the particle size distribution curve of the milled material through Monte Carlo simulation, and output it to the mixing plant control system. The specific steps include: By statistically analyzing different material types, we obtain the total volume of milled material, the threshold of the gradation range of recycled aggregate, and the actual material inventory data of the mixing plant; we establish a dynamic adjustment model for gradation parameters, and based on the current total volume of milled material and the actual material inventory data of the mixing plant, we dynamically form acceptable gradation range constraints and obtain the gradation optimization parameter matrix. The spatial distribution characteristics of the voxel grid in the milled area are obtained by using the voxel grid in the milled area. Based on the spatial distribution characteristics of the voxel grid in the milled area and the gradation optimization parameter matrix, a three-dimensional probability density function is established. Based on the three-dimensional probability density function, an adaptive Monte Carlo sampling strategy is adopted to increase sampling points in areas with high spatial density and reduce sampling points in areas with low density. At the same time, a Markov chain mechanism is introduced to make each sampling result correlated with the previous sampling result, thereby generating and obtaining the probability distribution field of milled material particle size. The probability distribution field of milled aggregate particle size is mapped to a two-dimensional gradation curve space, and the expected value and variance of the throughput of each particle size are calculated. Based on the expected value and variance of the throughput of each particle size, the threshold of the gradation interval of recycled aggregate is used as the boundary condition. The membership function of the gradation conformity is established by using the fuzzy comprehensive evaluation method. The simulation results are evaluated in multiple dimensions to obtain the set of optimized gradation curve schemes. Obtain the operating parameters of the batching plant equipment; use the dynamic adjustment model of gradation parameters to correct the set of optimized gradation curve schemes, and construct a matching degree evaluation model with the corrected optimized gradation curve schemes and the operating parameters of the batching plant equipment; use the matching degree evaluation model to comprehensively consider the operating conditions of the mixing machine, screen the gradation schemes with the equipment compliance rate in the optimal range, and generate a set of gradation control instructions; input the set of gradation control instructions into the batching plant control system.

10. A bridge deck smoothness variable control system based on point cloud data processing, characterized in that, The system employs the bridge deck smoothness variable control method based on point cloud data processing as described in any one of claims 1 to 9, and the system comprises: The vehicle-mounted road surface scanning module is used to acquire road surface point cloud data. It integrates a lidar, a first GNSS receiver, and an inertial measurement unit (IMU). The vehicle-mounted road surface scanning module is installed on a vehicle for use. The point cloud data processing module is used to preprocess the road surface point cloud data, fuse GNSS trajectory and IMU attitude data, generate continuous three-dimensional trajectory lines, generate the original road surface model based on the road surface point cloud data, and extract road surface distress features. The optimal milling thickness module is used to determine the constraints, including flatness, milling depth, and material waste. The NSGA-II genetic algorithm is used to construct a multi-objective optimization function with tangent length and longitudinal slope change rate as variables to determine the optimal milling thickness and obtain the optimal design 3D model. The milling depth adjustment module is used to automatically adjust the fine milling depth of a milling machine equipped with a second GNSS receiver and a GX-60 control center.

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