Three-dimensional reconstruction method and system for corrosion morphology of anchor rod based on three-dimensional laser scanning

Abstract

The invention discloses an anchor rod corrosion morphology three-dimensional reconstruction method and system based on three-dimensional laser scanning. The method comprises the following steps: acquiring anchor rod surface point cloud data through three-dimensional laser scanning and preprocessing the anchor rod surface point cloud data; performing serialized slicing processing on the point cloud, and establishing a local coordinate system axially aligned with the anchor rod; adopting an improved RANSAC (Random Sample Consensus) algorithm with adjacent slice continuity constraint, fitting the contour of the non-rusted reference circle piece by piece, and reconstructing a reference geometric shape; the corrosion degree is quantified by calculating the corrosion deviation value of the actual contour point and the reference circle, and is converted into polar coordinates to analyze circumferential distribution; and on the basis of the corrosion deviation value, a three-dimensional morphology cloud picture and a polar coordinate expansion picture are generated through bidirectional dynamic color mapping, and multi-dimensional visualization of the corrosion morphology is achieved. According to the method, the limitation that original contour data cannot be obtained through a traditional method is overcome, accurate quantification and reconstruction of anchor rod corrosion under the standard-free condition are achieved, and high robustness and engineering applicability are achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of three-dimensional reconstruction, and particularly relates to a three-dimensional reconstruction method and system for anchor rod corrosion morphology based on three-dimensional laser scanning. BACKGROUND

[0002] As a widely used active supporting structure in geotechnical engineering such as slope, tunnel and foundation pit, the long-term durability of metal anchor rod is directly related to the safety of the project. In particular, the free section length of the end-anchored anchor rod is large and lacks mortar protection, and the rod body in a long-term tensile state is prone to electrochemical corrosion and stress corrosion in a water-containing and oxygen-containing environment. This corrosion process will lead to a decrease in the effective cross-sectional area of the anchor rod and degradation of the mechanical properties, significantly reducing its bearing capacity, and thus may cause serious consequences such as instability of the supporting project.

[0003] Due to the randomness and non-uniformity of the corrosion process, local pits, strip corrosion belts and necking corrosion and other complex morphologies are formed on the surface of the anchor rod. These corrosion morphologies will significantly change the stress distribution characteristics of the anchor rod, and in a tensile state, stress concentration will occur in the local corrosion area, greatly increasing the risk of brittle fracture of the anchor rod. Therefore, accurate characterization and reconstruction of the corrosion morphology of the anchor rod are of great engineering significance for scientific evaluation of the residual bearing capacity and service life of the in-service anchor rod.

[0004] At present, the traditional anchor rod corrosion measurement method mainly relies on vernier caliper, weighing method and other means, and through these methods, macroscopic parameters such as corrosion rate and diameter reduction value can be obtained. However, due to the limitation of measurement accuracy and sampling density, the traditional method cannot completely characterize the complex corrosion morphology of the anchor rod surface, and it is more difficult to realize accurate quantification of the corrosion distribution, which cannot meet the high-precision demand of modern engineering for anchor rod corrosion evaluation.

[0005] In recent years, as a non-contact high-precision measurement method, three-dimensional laser scanning technology has shown significant advantages in obtaining object surface profile data. However, in the practical application of anchor rod corrosion morphology reconstruction, this technology still faces two key technical problems: first, three-dimensional scanning can only obtain the surface profile data of the anchor rod, and cannot directly represent the corrosion degree, and lacks effective means to intuitively display the corrosion distribution of the whole rod; second, corrosion quantification needs to be compared with the original profile data, but the in-service anchor rod excavated on site has been in service for decades, and its original profile data cannot be obtained. In addition, the bending deformation of the in-service anchor rod under long-term load will introduce significant errors when using the surface profile data of the same type of new anchor rod as the reference, resulting in major technical bottlenecks in accurate characterization of corrosion morphology and accurate representation of corrosion degree.

[0006] In view of the above technical problems, it is urgent to develop an innovative method that can realize accurate reconstruction and quantitative evaluation of anchor rod corrosion morphology without relying on original uncorroded data, and provide reliable technical support for safety evaluation of service anchor rods. SUMMARY

[0007] Therefore, the purpose of the present application is to provide a three-dimensional reconstruction method and system for anchor rod corrosion morphology based on three-dimensional laser scanning, which realizes accurate quantification and three-dimensional reconstruction of anchor rod corrosion under non-reference conditions through three-dimensional laser scanning and slice analysis, and has good robustness and engineering applicability.

[0008] To achieve the above purpose, the present application provides the following technical scheme: The three-dimensional reconstruction method for anchor rod corrosion morphology based on three-dimensional laser scanning provided by the present application comprises the following steps: Step 1: Collecting three-dimensional point cloud data of the anchor rod surface, and pre-processing the three-dimensional point cloud data; Step 2: Slice processing of the pre-processed three-dimensional point cloud data, including establishing a local coordinate system aligned with the anchor rod axial direction, and serializing slicing along the anchor rod axial direction at a fixed interval to obtain two-dimensional contour coordinate data of each slice; Step 3: For the two-dimensional contour coordinate data of each slice, a random sample consensus (RANSAC) algorithm with continuity constraint is used to fit the uncorroded reference circle to obtain the reference center coordinates of each slice; Step 4: Based on the reference center coordinates and the nominal radius, the corrosion deviation of each data point on each slice is calculated; Step 5: Based on the corrosion deviation, three-dimensional morphology reconstruction and visualization are performed to generate an anchor rod corrosion three-dimensional morphology cloud chart and a polar coordinate development chart.

[0009] Further, the establishment of the local coordinate system comprises: using the least squares method to fit the overall point cloud to obtain a plane equation, and establishing a local coordinate system based on the normal vector of the plane equation.

[0010] Further, the RANSAC algorithm with continuity constraint in step 3 comprises: for the first slice, performing standard RANSAC circle fitting; for the subsequent slices, taking the center of the previous slice as the position prior, and introducing a circle center offset constraint.

[0011] Further, the circle center offset constraint comprises: calculating the Euclidean distance between the current fitting circle center and the circle center of the previous slice, and if it exceeds the maximum allowed threshold, the circle center position is modified.

[0012] Further, the calculation of the corrosion deviation comprises: for each data point, calculating the Euclidean distance to the corresponding reference center, subtracting the nominal radius to obtain the corrosion deviation.

[0013] Furthermore, step 4 also includes converting the two-dimensional contour coordinate data of each slice into polar coordinates.

[0014] Furthermore, the three-dimensional morphology reconstruction and visualization in step 5 includes: constructing a dynamic color mapping system based on the corrosion deviation to generate a three-dimensional morphology cloud map and a polar coordinate unfolded map; The dynamic color mapping system uses bidirectional color mapping, where negative value areas represent rusted areas and use a gradient from dark purple to light yellow, while positive value areas represent raised areas and use a gradient from white to dark blue.

[0015] The present invention provides a three-dimensional reconstruction system for anchor bolt corrosion morphology based on three-dimensional laser scanning, which includes a data acquisition module, a preprocessing module, a slicing module, a contour reconstruction module, a quantization calculation module, and a visualization module. The data acquisition module is used to acquire three-dimensional point cloud data of the anchor bolt surface; The preprocessing module is used to preprocess the three-dimensional point cloud data; The slicing processing module is used to slice the preprocessed three-dimensional point cloud data, including establishing a local coordinate system aligned with the anchor bolt axis, and slicing sequentially along the anchor bolt axis at fixed intervals to obtain the two-dimensional contour coordinate data of each slice. The contour reconstruction module is used to fit the uncorroded reference circle using the Random Sample Consensus (RANSAC) algorithm with continuity constraints to the two-dimensional contour coordinate data of each slice, so as to obtain the reference circle center coordinates of each slice. The quantitative calculation module is used to calculate the corrosion deviation of each data point on each slice based on the reference center coordinates and nominal radius. The visualization module is used to reconstruct and visualize the three-dimensional morphology based on the corrosion deviation, and generate a three-dimensional morphological cloud map and polar coordinate unfolded map of the anchor bolt corrosion.

[0016] Furthermore, the slicing processing module includes a plane fitting unit, which is used to perform plane fitting on the overall point cloud using the least squares method to obtain a plane equation, and establish a local coordinate system based on the normal vector of the plane equation.

[0017] Furthermore, the contour reconstruction module is configured as follows: for the first slice, standard RANSAC circle fitting is performed; for subsequent slices, the center of the previous slice is used as the position prior, and a center offset constraint is introduced; the Euclidean distance between the current fitted center and the center of the previous slice is calculated, and if it exceeds the maximum allowable threshold, the center position is corrected.

[0018] The beneficial effects of this invention are as follows: The present invention provides a three-dimensional reconstruction method and system for anchor bolt corrosion morphology based on three-dimensional laser scanning. This method is an anchor bolt corrosion morphology reconstruction method that does not rely on the original uncorroded data, and solves the problem of missing corrosion reference caused by the inability to obtain the original contour data of anchor bolts in service in the field. The reference geometry of the uncorroded state is reconstructed from the corrosion contour through an improved RANSAC algorithm.

[0019] By establishing a precise quantitative system for the degree of anchor corrosion, we can overcome the shortcomings of traditional measurement methods, such as insufficient accuracy and limited characterization. This system enables multi-dimensional quantitative characterization from macroscopic corrosion rate to microscopic corrosion depth, providing accurate data support for assessing the remaining bearing capacity of anchors in service.

[0020] By developing a multi-dimensional visualization technology for anchor bolt corrosion morphology, the technical bottleneck of making it difficult to intuitively display the corrosion distribution of the entire bolt using 3D scanning data is solved. Through the joint analysis of 3D morphology cloud maps and polar coordinate unfolded maps, a combination of global understanding and local quantitative analysis of corrosion morphology is achieved.

[0021] By constructing a robust analysis method adapted to actual engineering conditions, and addressing complex situations such as bending deformation and uneven local corrosion of anchor bolts in service, the method ensures stable and reliable analysis results even under non-ideal conditions through continuity constraints and anomaly handling mechanisms.

[0022] This method innovatively performs serialized slicing and coordinate unification of 3D data preprocessing techniques, slicing the 3D point cloud along the anchor bolt axis at fixed intervals, transforming the complex 3D spatial problem into a series of 2D contour analysis problems. Through least-squares plane fitting and coordinate transformation, a local coordinate system aligned with the actual spatial posture of the anchor bolt is established, effectively overcoming the reference error caused by anchor bolt bending or tilting.

[0023] An improved RANSAC robust fitting algorithm incorporating continuity constraints is proposed. Based on the traditional RANSAC algorithm, it innovatively introduces spatial continuity constraints between adjacent slices, using the center position of the previous slice as prior information, significantly improving the stability and accuracy of the fitting. A center offset threshold constraint mechanism (e.g., 0.3 mm) is designed; when the fitted center deviates too much, automatic geometric correction is performed to ensure the smoothness and physical rationality of the center trajectory. A multi-index quality evaluation system (interior point ratio, residual statistics, center offset) is established to achieve automatic quality control and anomaly handling of the fitting results.

[0024] The bidirectional dynamic color mapping multi-dimensional visualization technology dynamically adjusts the color mapping interval according to the actual corrosion range to ensure optimal color contrast. It generates 3D morphology cloud maps and polar coordinate unfolded maps, enabling a multi-dimensional assessment that combines global understanding of corrosion morphology with local quantitative analysis.

[0025] A complete technical system, from microscopic quantification to macroscopic reconstruction, has been established, encompassing point cloud acquisition, slicing processing, contour fitting, corrosion quantification, and 3D reconstruction, enabling full-process digital analysis of anchor bolt corrosion. The abstract degree of corrosion is transformed into a precise numerical indicator (corrosion deviation Δ), providing a quantitative basis for service status assessment and remaining service life prediction.

[0026] In terms of engineering practicality and adaptability, the adjustable slice spacing (0.5mm-2mm) allows it to adapt to application scenarios with varying precision requirements. The algorithm exhibits strong robustness, effectively handling contour defects and data noise caused by severe corrosion. It is applicable to both smooth and ribbed anchor bolts, demonstrating broad engineering applicability.

[0027] The above and other objects, advantages, and features of the present invention will be more fully set forth and demonstrated through the following detailed description of specific embodiments in conjunction with the accompanying drawings. Those skilled in the art, upon referring to the following detailed description and the accompanying drawings, will be able to better understand and realize the above advantages of the present invention. Other objects, features, and advantages of the present invention will become clearer after being described in detail in the detailed description section in conjunction with the accompanying drawings. Attached Figure Description

[0028] To make the objectives, technical solutions, and beneficial effects of this invention clearer, the following drawings are provided for illustration.

[0029] Figure 1 Flowchart for reconstructing the three-dimensional morphology of anchor bolt corrosion; Figure 2 Flowchart for slicing and processing 3D point cloud data of anchor bolts; Figure 3 This is a flowchart of the contour reconstruction of an uncorroded anchor bolt based on the improved RANSAC algorithm. Figure 4 Flowchart for quantifying the degree of corrosion on anchor bolt cross-sections; Figure 5 Flowchart for 3D morphological reconstruction and visualization of anchor bolt corrosion; Figure 6 A schematic diagram of anchor bolt point cloud slice processing; Figure 7 Slice the point cloud data for the anchor bolt; Figure 8 Calculation of corrosion amount for anchor bolt cross-section; Figure 9 A three-dimensional topographic cloud image of anchor bolt corrosion; Figure 10 This is a planar development diagram of the rust morphology of the anchor bolt. Detailed Implementation

[0030] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand and implement the present invention. However, the embodiments described are not intended to limit the present invention.

[0031] Example 1 The three-dimensional reconstruction method for anchor bolt corrosion morphology based on three-dimensional laser scanning provided in this embodiment specifically includes the following steps: Step 1: Collect three-dimensional point cloud data of the anchor bolt surface and preprocess the three-dimensional point cloud data; Step 2: Perform slicing processing on the preprocessed 3D point cloud data, including establishing a local coordinate system aligned with the anchor bolt axis, and serializing slices along the anchor bolt axis at fixed intervals to obtain the 2D contour coordinate data of each slice; Step 3: For the two-dimensional contour coordinate data of each slice, the Random Sample Consensus (RANSAC) algorithm with continuity constraints is used to fit the uncorroded reference circle to obtain the reference circle center coordinates of each slice. Step 4: Based on the reference center coordinates and nominal radius, calculate the corrosion deviation of each data point on each slice; Step 5: Based on the corrosion deviation, perform three-dimensional morphology reconstruction and visualization to generate a three-dimensional morphology cloud map and polar coordinate unfolded map of the anchor bolt corrosion.

[0032] The preprocessing described in step 1 of this embodiment includes noise reduction, point filling, and simplification.

[0033] In this embodiment, step 2, establishing a local coordinate system, includes: performing plane fitting on the overall point cloud using the least squares method to obtain a plane equation, and establishing a local coordinate system based on the normal vector of the plane equation.

[0034] The RANSAC algorithm with continuity constraints described in step 3 of this embodiment includes: for the first slice, performing standard RANSAC circle fitting; for subsequent slices, using the center of the previous slice as a position prior and introducing a center offset constraint.

[0035] The center offset constraint in this embodiment includes: calculating the Euclidean distance between the current fitted center and the center of all previous circles; if it exceeds the maximum allowable threshold, the center position is corrected.

[0036] In step 4 of this embodiment, the calculation of the corrosion deviation includes: for each data point, calculating its Euclidean distance to the center of the corresponding reference circle, subtracting the nominal radius, and obtaining the corrosion deviation.

[0037] Step 4 in this embodiment further includes converting the two-dimensional contour coordinate data of each slice into polar coordinates.

[0038] Step 5 in this embodiment, the three-dimensional morphology reconstruction and visualization, includes: constructing a dynamic color mapping system based on the corrosion deviation, and generating a three-dimensional morphology cloud map and a polar coordinate unfolded map.

[0039] The dynamic color mapping system described in this embodiment uses bidirectional color mapping, where negative value areas represent rusted areas and use a gradient from dark purple to light yellow, and positive value areas represent raised areas and use a gradient from white to dark blue.

[0040] This embodiment also provides a three-dimensional reconstruction system for anchor bolt corrosion morphology based on three-dimensional laser scanning, including: The data acquisition module is used to acquire three-dimensional point cloud data of the anchor bolt surface; The preprocessing module is used to preprocess the three-dimensional point cloud data; The slicing module is used to slice the preprocessed 3D point cloud data, including establishing a local coordinate system aligned with the anchor bolt axis, and slicing sequentially along the anchor bolt axis at fixed intervals to obtain the 2D contour coordinate data of each slice. The contour reconstruction module is used to fit the uncorroded reference circle using the Random Sample Consensus (RANSAC) algorithm with continuity constraints to the two-dimensional contour coordinate data of each slice, so as to obtain the reference circle center coordinates of each slice. The quantization calculation module is used to calculate the corrosion deviation of each data point on each slice based on the reference center coordinates and nominal radius. The visualization module is used to reconstruct and visualize the three-dimensional morphology based on the corrosion deviation, and generate a three-dimensional morphological cloud map and polar coordinate unfolded map of the anchor bolt corrosion.

[0041] The preprocessing module in this embodiment includes a noise reduction unit, a point filling unit, and a simplification unit.

[0042] The slicing module in this embodiment includes a plane fitting unit, which is used to perform plane fitting on the overall point cloud using the least squares method to obtain a plane equation, and establish a local coordinate system based on the normal vector of the plane equation.

[0043] In this embodiment, the contour reconstruction module is configured as follows: for the first slice, standard RANSAC circle fitting is performed; for subsequent slices, the center of the previous slice is used as the position prior, and a center offset constraint is introduced.

[0044] The contour reconstruction module in this embodiment is further configured to: calculate the Euclidean distance between the current fitted circle center and the center of the previous all circles; if the distance exceeds the maximum allowable threshold, then correct the circle center position.

[0045] In this embodiment, the quantization calculation module is configured to: for each data point, calculate its Euclidean distance to the center of the corresponding reference circle, subtract the nominal radius, and obtain the corrosion deviation.

[0046] The quantization calculation module in this embodiment is also configured to convert the two-dimensional contour coordinate data of each slice into polar coordinates.

[0047] The visualization module in this embodiment includes a dynamic color mapping unit, which is used to construct a two-way color mapping system based on the corrosion deviation amount to generate a three-dimensional morphology cloud map and a polar coordinate unfolded map.

[0048] In this embodiment, the bidirectional color mapping system uses negative value areas to represent rusted areas, using a gradient color from dark purple to light yellow, and positive value areas to represent raised areas, using a gradient color from white to dark blue.

[0049] Example 2 The method provided in this embodiment is based on the cross-disciplinary integration of spatial geometry, statistical robust estimation, and computer graphics. Its technical principle begins with acquiring high-precision point cloud data of the anchor bolt surface through optical 3D measurement; establishing a local coordinate system aligned with the anchor bolt axis based on least-squares plane fitting and coordinate transformation principles; and processing the point cloud data into slices, transforming the 3D spatial positioning problem into a series of 2D contour analyses. The core innovation lies in employing a Random Sample Consensus Algorithm with continuity constraints (RANSAC algorithm). Through iterative random sampling and model verification, it resists interference from rust anomalies while introducing spatial constraints between adjacent slices, ensuring the smoothness and physical rationality of the fitted circle's trajectory. Based on differential geometry principles, by calculating the rust deviation of each data point relative to the fitted reference circle, the complex rust morphology is transformed into a scalar field quantization problem. Furthermore, polar coordinate transformation is used to expand the circumferential rust distribution characteristics from a 2D plane to a 1D angular domain, achieving multi-dimensional analysis of the rust pattern. Finally, a two-way color mapping system was constructed based on numerical interpolation theory. The three-dimensional cylindrical surface was unfolded into a two-dimensional plane through conformal transformation. Combined with the principle of optical rendering, a three-dimensional morphological cloud map and polar coordinate unfolded map that intuitively reflect the degree of corrosion of the entire rod were generated, forming a complete technical system from micro-corrosion quantification to macro-morphological reconstruction.

[0050] like Figure 1 As shown in this embodiment, a quantitative reconstruction method for anchor bolt corrosion morphology based on 3D point cloud and robust fitting is provided. By sequentially slicing the 3D point cloud of the corroded anchor bolt, the improved RANSAC algorithm is used to reconstruct the baseline contour of the uncorroded anchor bolt slice by slice, and the corrosion amount and 3D morphology are accurately calculated accordingly. The specific implementation steps are as follows: S1. Anchor Bolt 3D Point Cloud Data Acquisition and Preprocessing A high-precision 3D laser scanner was used to scan the surface of the corroded anchor bolt to obtain complete 3D point cloud coordinate data [X, Y, Z]. Subsequently, the raw point cloud data was preprocessed, including noise reduction, point filling, and simplification, to eliminate errors and ensure the integrity and accuracy of the data.

[0051] S2, Anchor Bolt 3D Point Cloud Data Slicing Processing The preprocessed 3D point cloud is sliced ​​along the anchor bolt axis at fixed intervals, transforming the 3D problem into a series of 2D cross-sectional profile analysis problems. The specific sub-steps are as follows: (1) Plane fitting and coordinate system establishment: The least squares method is used to perform plane fitting on the overall point cloud to obtain the plane equation describing the spatial attitude of the anchor bolt: Z = aX + bY + c In the formula, [X, Y, Z] represents the point cloud coordinate dataset, and a, b, c are the coefficients of the plane equation. Based on the normal vector of this plane, a local coordinate system is established with the centroid of the anchor bolt as the origin and aligned with the axis of the anchor bolt.

[0052] (2) Coordinate transformation: The point cloud [X, Y, Z] in the global coordinate system is transformed into the local coordinate system mentioned above through rotation and translation transformation.

[0053] (3) Sequence slicing: In the local coordinate system, a series of parallel slice planes are generated along the anchor bolt axis at a set interval. All point clouds within a certain thickness on both sides of each slice plane are extracted to form the two-dimensional cross-sectional contour coordinate data [Xi, Zi] at that location, where i is the slice number.

[0054] S3. Contour Reconstruction of Uncorroded Anchor Bolts Based on Improved RANSAC Algorithm For each slice, the two-dimensional contour point set [Xi, Zi] is obtained, and a Random Sample Consensus (RANSAC) algorithm with continuity constraints is used to fit the baseline circular contour of the uncorroded section. The specific sub-steps are as follows: (1) Parameter initialization: Set the RANSAC algorithm parameters, including the maximum number of iterations, the inlier distance threshold, and the minimum number of inliers, and use the nominal radius of the anchor as a fixed radius constraint.

[0055] (2) Continuity constraint fitting: For the first slice (i=1), perform standard RANSAC circle fitting to obtain the initial circle center (Xc1, Zc1). For the subsequent i-th (i>1) slice, the circle center (Xc1, Zc1) of the previous slice is... i-1 , Zc i-1As a positional prior, a center offset constraint is introduced in RANSAC sampling or model validation. The Euclidean distance between the current fitted center and the center of the previous slice is calculated. If the distance exceeds the maximum allowable threshold (e.g., 0.3 mm), the fitted center of the previous slice is inherited as the center of the current slice, ensuring a smooth transition between the centers of adjacent slices.

[0056] In this embodiment, the center position correction is as follows: if the Euclidean distance between the previous fitted center and the current fitted center on the [X, Z] plane exceeds the maximum allowable threshold (e.g., 0.3 mm), it indicates that the current fitted center position has a large deviation. In this case, the fitted center of the previous slice is directly inherited as the center of the current slice to ensure a smooth transition between the centers of adjacent slices.

[0057] (3) Quality assessment and anomaly handling: The fitting results of each slice are assessed for quality, including the proportion of inliers, the average residual, and the center offset. If the fitting fails or the quality is not up to standard, the center of the previous slice is inherited as the current result to ensure the robustness of the reconstruction process.

[0058] (4) Save the fitted circle center dataset: Finally, a smoothly changing reference circle center coordinate array [Xc, Zc] corresponding to the slice sequence is obtained.

[0059] S4. Quantitative Calculation of Corrosion Degree of Anchor Bolt Section The degree of corrosion is precisely quantified by comparing the actual corroded cross-section with the fitted reference circle. The specific sub-steps are as follows: (1) Calculation of corrosion deviation: For each data point (Xi, Zi) on each slice i, calculate the Euclidean distance Di to the corresponding fitting circle center (Xci, Zci) and compare it with the nominal radius R to obtain the corrosion deviation Δi.

[0060] Δi = Di - R In the formula, Di is the Euclidean distance from each data point of slice i to the fitting circle center (Xci, Zci); (Xi, Zi) are the point cloud coordinates of anchor slice i; (Xci, Zci) are the fitting circle center coordinates of slice i; Δi is the corrosion deviation of slice i.

[0061] (2) Polar coordinate transformation: To facilitate the analysis of the distribution of corrosion in the circumferential direction, the Cartesian coordinates (Xi, Zi) of the contour points on each slice i are transformed into polar coordinates (θi, ρi). In the formula, θi is the polar angle of the point on slice i relative to the center of the circle (adjusted to the range of [0, 2π]), and ρi is the radial distance on slice i (i.e., the aforementioned D).

[0062] (3) Data integration: The point cloud coordinates [X, Y, Z] of all slices, the corresponding corrosion deviation [Δ], and the polar coordinates [θ, Y, ρ] are integrated to form a global dataset for 3D reconstruction.

[0063] S5. Reconstruction and Visualization of 3D Morphology of Anchor Bolt Corrosion Based on integrated global data, a three-dimensional morphological map is generated that intuitively reflects the degree of corrosion across the entire rod. The specific sub-steps are as follows: (1) Dynamic color mapping: Construct a dedicated two-way color mapping bar. Dynamically set the color range according to the distribution range of global corrosion amount [Δ] [min(Δ), max(Δ)]. Design a gradient color from dark purple (severe corrosion) to light yellow (slight corrosion) to represent negative values ​​(corrosion area); design a gradient color from white to dark blue to represent positive values ​​(raised area).

[0064] (2) Generation of three-dimensional topography cloud map: In three-dimensional space, the topography of the anchor bolt is represented by point cloud [X, Y, Z]. The color of each point is rendered according to its corrosion deviation Δ and the above color mapping bar to generate a three-dimensional topography cloud map of anchor bolt corrosion.

[0065] (3) Polar coordinate unfolding generation: The 3D topography is segmented according to the anchor bolt length. For each segment, the polar coordinates [θ, Y, ρ] are unfolded into a planar diagram. On this planar diagram, the color of the points is also determined by Δ, thereby generating a 2D cloud map of the corrosion distribution unfolded along the circumference, realizing multi-dimensional visualization of the corrosion topography. Example 3 This embodiment further illustrates the method with specific illustrations and implementation details. Specifically: 1. Anchor Bolt 3D Point Cloud Data Acquisition and Preprocessing A high-precision 3D laser scanner was used to perform a full-range scan of the corroded anchor bolt surface, with a scanning accuracy set to 0.02mm to ensure that minute corrosion features could be captured. During the scanning process, complete 3D point cloud coordinate data [X, Y, Z] of the anchor bolt surface was obtained by taking pictures from multiple angles, and the coverage area of ​​a single scan was no less than 200mm × 150mm.

[0066] The acquired raw point cloud data underwent systematic preprocessing. Noise denoising: Statistical filtering algorithms were used to remove outliers whose distance from the mean exceeded two standard deviations. Data imputation: Based on the spatial relationships between adjacent point clouds, missing regions were filled using interpolation. Data simplification: While preserving features, curvature sampling methods were used to optimize the point cloud density to 60%-80% of the original data. The preprocessed point cloud data ensured no holes, no overlaps, and no noise interference, providing a high-quality data foundation for subsequent analysis.

[0067] 2. Anchor Bolt 3D Point Cloud Data Slicing Processing The preprocessed 3D point cloud data [X, Y, Z] is sequentially sliced ​​along the anchor bolt axis (Y-axis direction) at fixed intervals of 1 mm. The specific process is as follows: Figure 2 As shown, the implementation steps are as follows: (1) Point cloud data reading and plane fitting First, the 3D point cloud data file of the anchor bolt is read to obtain the spatial coordinates [X, Y, Z] of each sampling point. The scattered point cloud is fitted to a plane using the least squares method to establish a mathematical model describing the overall spatial attitude of the anchor bolt. The coefficient matrix of the plane equation is constructed, and the optimal plane parameters are solved using linear regression. The coefficients a, b, and c of the plane equation Z = aX + bY + c are obtained. The specific parameters of the fitted plane equation are output, providing a benchmark for subsequent coordinate transformations.

[0068] (2) Establishment of local coordinate system A local coordinate system for the anchor bolt is constructed based on the fitted plane to achieve the transformation from global to local coordinates. The unit normal vector n = [-a, -b, 1] / ‖[-a, -b, 1]‖ of the fitted plane is calculated to determine the Z-axis direction of the local coordinate system; the Y-axis direction of the local coordinate system is defined by the cross product of the X-axis direction vector and the normal vector; a complete 3×3 rotation matrix R = [u, v,n] is constructed to realize the transformation relationship from the global to the local coordinate system; the centroid coordinates C = [Xmean,Ymean,Zmean] of the point cloud data are calculated as the reference point for coordinate system translation.

[0069] Where u represents the unit direction vector of the X-axis in the local coordinate system; v represents the unit direction vector of the Y-axis in the local coordinate system; n represents the unit direction vector of the Z-axis in the local coordinate system; Xmean represents the centroid of the point cloud data in the X direction of the global coordinate system; Ymean represents the centroid of the point cloud data in the Y direction in the global coordinate system; Zmean represents the centroid of the point cloud data in the Z direction in the global coordinate system; (3) Coordinate transformation and data alignment Perform spatial coordinate transformation on the point cloud data. Translate the original point cloud data to a relative coordinate system with the centroid as the origin; apply a rotation matrix to transform the translated point cloud to a local coordinate system; generate a dual coordinate representation containing both local and global coordinates for easier subsequent analysis and verification.

[0070] (4) Setting and determining the range of slicing parameters Configure key parameters for slice processing and determine the slice range: Set the slice spacing to 1mm to ensure that the slice density meets the accuracy requirements; extract the coordinate component values ​​perpendicular to the slice direction (Y-axis direction) in the local coordinate system; calculate the minimum value Ymin and the maximum value Ymax of the coordinate in this direction, and determine the complete slice range Yrange=[Ymin, Ymax]; slice number Nslices=Yrange / spacing+1; establish a global offset correction mechanism to ensure the continuity and standardization of slice numbering.

[0071] (5) Iterative slicing and data output Perform continuous slicing along the set Y-axis direction and save the results, such as... Figure 6 As shown.

[0072] In this embodiment, the slice thickness refers to the thickness of several anchor bolt slices obtained after the anchor bolts are sliced ​​at 1mm intervals. The cutting positions in the figure are indicated by cutting lines.

[0073] Generate a continuous sequence of slice boundaries according to the preset slice spacing; For each slice interval, filter all point cloud data falling within that thickness range [X] i Z i Simultaneously, it records the coordinate information of the selected points in the local and global coordinate systems; generates slice data files according to a unified naming rule, with the format "slicev start position to end position.csv"; automatically creates an output directory and saves all slice data files to ensure the systematic organization of the data.

[0074] According to the coordinate data of the cross-section of the anchor bolt slice [X i Z i The cross-section of the anchor bolt can be drawn, such as... Figure 7 As shown, the uncorroded outline is as follows Figure 7 In Figure (a), the theoretical benchmark for algorithm reconstruction is shown. It is not directly derived from the scanned data, but rather from an ideal circle fitted by applying the "RANSAC algorithm with continuity constraints" (see step S3) to the point cloud of the slice. The center of this circle is (Xci, Zci), and its radius is the nominal radius R of the anchor bolt. The uncorroded profile shown in the figure is drawn from this standard circle, representing the original geometry of the anchor bolt at this section when it is uncorroded. The corroded profile is as follows: Figure 7 (b) is derived directly from the scan data. It is drawn by scattering the two-dimensional cross-sectional profile point set {Xi, Zi} obtained in step 2 according to the spatial position of the profile points in the plane, thereby showing the cross-sectional morphological changes caused by corrosion.

[0075] Observation of the cross-section revealed that the outline of the uncorroded section was relatively complete, close to a standard circle, and the longitudinal and transverse ribs (crescent ribs) of the anchor bolt were clearly visible. Figure 7 (a) shows the uncorroded outline; while corrosion causes local defects in the anchor bolt cross-section, and the shape deviates from the standard circle. Figure 7 (b) shows the rust outline.

[0076] 3. Contour Reconstruction of Uncorroded Anchor Bolts Based on Improved RANSAC Algorithm For each slice, the two-dimensional contour point set [X i Z i The baseline circular profile before corrosion was reconstructed using the RANSAC algorithm with continuity constraints. The specific process is as follows: Figure 3 As shown, the implementation steps are as follows: (1) Parameter initialization and output configuration Set the key parameters for the circle fitting process: Define RANSAC algorithm parameters, maximum number of iterations 2000, inlier distance threshold 0.1mm, minimum number of inlier points 10; set the original radius of the anchor rod to 11.75mm (corresponding to Φ25mm anchor rod); establish quality evaluation standards: average residual threshold 0.3mm, minimum inlier ratio 20%, maximum center offset 0.3mm.

[0077] (2) Ordering of sliced ​​data The input slice data files are systematically organized as follows: all CSV format slice files in the directory are scanned; the starting position number in the file name is extracted using regular expressions; the files are sorted in ascending order according to the starting position value to ensure that the processing order corresponds to the physical location; the total number of slices is counted to establish an index foundation for subsequent iterative processing.

[0078] (3) Data storage structure and quality assessment system Initialize result storage and quality monitoring data structure: Create a center coordinate storage array [X c Z c The dimension is the number of slices n of the anchor rod; a memory variable for the center of each slice is set up for continuity constraint; a structured quality assessment data table is constructed, which includes fields such as slice number, file name, number of interior points, average residual, maximum residual, center offset, and fitting quality indicator.

[0079] (4) Iterative slice processing main loop Perform circle fitting and quality assessment sequentially for each slice: ① Data Reading and Preprocessing: Read the CSV file of the current slice i and extract the coordinate data (X) of the contour points on slice i. i Z i); Filter out any invalid NaN data points; Record the total number of data points in the current slice i to the quality evaluation table.

[0080] ② RANSAC-based circle center fitting: A differentiated fitting strategy is adopted based on the slice position. For the first slice, unconstrained RANSAC fixed-radius circle fitting is used; for subsequent slices, the center of the previous slice is introduced as a positional constraint to enhance the continuity of the fitting; a dedicated RANSAC function is called to calculate the center coordinates (X, Y, X) of the current slice i. ci Z ci ).

[0081] ③ Continuity constraint of the center position, implement the spatiotemporal continuity guarantee mechanism of the center position: calculate the Euclidean distance between the current fitted center and the center of the previous slice; when the center offset exceeds the maximum allowable threshold, inherit the fitted center of the previous slice as the center of the current slice; limit the center position within the maximum allowable offset range to ensure the smooth transition of the center of adjacent slices.

[0082] ④ Abnormal situation handling mechanism, establishing a strategy for handling fitting failure: Detect the validity of the RANSAC fitting output results; when fitting fails, automatically inherit the previous slice center position (X). ci-1 Z ci-1 Record the current result; record any abnormal states and mark them as poor fits in the quality assessment table.

[0083] (5) Quantitative evaluation of fit quality A multi-dimensional quality assessment is performed on the fitting results for each slice. The proportion of valid data points that meet the distance threshold is calculated; residual statistics are calculated, including the mean residual and the maximum residual of the inliers; the relative offset of the circle center is recorded to evaluate the continuity of position.

[0084] The fitting quality is judged based on multiple conditions: check whether the average residual exceeds the quality threshold of 0.3 mm; verify whether the proportion of inliers meets the minimum requirement of 20%; confirm whether the center offset is within the allowable range of 0.3 mm; and evaluate the fitting quality of the slices based on the above conditions.

[0085] (6) Update the system status after completing single-slice processing. Set the center coordinates (X) of the current slice i. ci Z ci Store the data in the corresponding position of the result array; update the memory variable for the center of each slice to provide constraints for the next slice processing; repeat the process until all slices are fitted.

[0086] Finally, the reference center coordinate array [X] corresponding to the slice sequence is obtained. c Z c This forms a smoothly changing circular trajectory, providing a geometric reference for corrosion calculation.

[0087] 4. Quantitative calculation of the degree of corrosion of anchor bolt cross-section By comparing the geometric differences between the actual corroded cross section and the fitted reference circle, the degree of corrosion can be accurately quantified. The specific process is as follows: Figure 4 As shown, the implementation steps are as follows: (1) Calculation of surface corrosion deviation For all data points (X) on each slice i i Z i ), calculate its distance to the corresponding fitted circle center (X). ci Z ci The Euclidean distance Di is calculated and compared with the nominal radius R to obtain the corrosion deviation Δi.

[0088] Δi = Di - R In the formula, D i For each data point in slice i, the distance from the center of the fitted circle (X) ci Z ci The Euclidean distance of (X) i Z i (X) represents the point cloud coordinates of anchor slice i; ci Z ci ) represents the coordinates of the fitted circle center of slice i; Δi represents the corrosion deviation of each data point on slice i.

[0089] (2) Polar coordinate transformation To facilitate the analysis of the circumferential distribution of corrosion, the Cartesian coordinates (X, X, Y) of the contour points on each slice i are used. i Z i Convert to polar coordinates (θ) i , ρ i The specific calculation formula is as follows: In the formula, θ i Let ρi be the polar angle of the point on slice i relative to the center of the circle (adjusted to the range [0, 2π]), and let ρi be the radial distance on slice i (i.e., the aforementioned D). Figure 8 As shown, Figure 8 The image shows a cross-sectional slice of a corroded anchor bolt. The dark blue area represents the remaining effective cross-section of the anchor bolt after corrosion, while the light blue area represents the cross-section lost due to corrosion.

[0090] In a two-dimensional plane, given the Cartesian coordinates (X, Z) of a point P and the coordinates of the center of a circle (Xc, Zc), the polar coordinates (θ, ρ) represent the angle and radial distance of point P relative to the center of the circle. The transformation formula is based on geometric relations: The radial distance ρ is the Euclidean distance from point P to the center of the circle (Xc, Zc), and is calculated using the following formula: The polar angle θ is the azimuth angle of point P relative to the center of the circle in the XZ plane. It is calculated using the four-quadrant arctangent function (acrtan) to ensure that the angle correctly covers all quadrants. The calculation formula is: The acrtan function returns values ​​in the range (-π, π). For ease of analysis, θ is adjusted to the range [0, 2π): if θ < 0, then θ = θ + 2π.

[0091] Calculation example: Suppose that the Cartesian coordinates of a point P on a slice are (Xi, Zi) = (12.5, 8.0), and the coordinates of the fitted circle center are (Xci, Zci) = (10.0, 10.0). Then: Calculate the relative coordinates: ΔX = Xi - Xci = 12.5 - 10.0 = 2.5, ΔZ = Zi - Zci = 8.0 - 10.0 = -2.0.

[0092] Calculate the radial distance ρi: Calculate the polar angle θi: Since θi < 0, adjust to the range [0, 2π): Finally, convert θi from radians to degrees. Therefore, the polar coordinates of point P are (θi, ρi) ≈ (321.34°, 3.2016).

[0093] In this cross-section, any point on the profile of the corroded area can be represented in polar coordinates as coordinates (θ, ρ). Here, θ is the polar angle of the point relative to the center of the circle, and ρ is the radial distance from the point to the center of the circle (i.e., the aforementioned parameter D). The corrosion depth deviation at this profile point is defined as Δ, and its specific calculation method can be found in the relevant formulas.

[0094] Through calculation Figure 8 At a certain angle θ i The radial distance ρi from the contour point to the center of the circle is calculated, and the corrosion deviation Δi of the contour point is also calculated to obtain the corrosion amount on the cross section at that angle; subsequently, the corrosion amount is calculated sequentially according to an angle step of 0.5°. Figure 8The corrosion deviation Δ of the anchor bolt cross-section profile points across all angular ranges is calculated; this yields the corrosion data for each profile point on the cross-section.

[0095] (3) Global data integration and statistics After all slice processing is completed, the data is summarized. Vertical stitching is used to merge the 3D point cloud data of all slices into a global point cloud dataset [X, Y, Z]; the corrosion data of all slices is merged into a global corrosion dataset [X, Z, Δ]; polar coordinate point cloud data and polar coordinate corrosion data [θ, Y, ρ] are merged simultaneously; global data statistics are output, including the total number of data points, providing a complete data foundation for subsequent 3D reconstruction.

[0096] 5. Reconstruction and visualization of the three-dimensional morphology of anchor bolt corrosion The corrosion depth is characterized by color variations, generating a three-dimensional morphological cloud map reflecting the overall corrosion level of the rod. The specific process is as follows: Figure 5 As shown, the implementation steps are as follows: (1) Construction of dynamic color mapping system Based on the actual distribution range [min(Δ), max(Δ)] of the global corrosion amount data [Δ], the color mapping interval is dynamically set. The position corresponding to the zero value is calculated, and the normalized position in the color mapping when the corrosion amount is zero is determined.

[0097] A color boundary benchmark was established between positive (raised) and negative (corrosion) values, and a two-way color mapping scheme specifically for corrosion characteristics was constructed. The negative value area (corrosion area) uses a 5-level gradient to represent different degrees of corrosion: dark purple (position 0): severe corrosion; rust red (position 0.15): obvious corrosion; orange (position 0.35): moderate corrosion; amber yellow (position 0.55): slight corrosion; light yellow (position 0.75): very slight corrosion. The positive value area (raised area) uses a 4-level gradient to represent material raised areas: white (position 0.8): zero value transition; light blue (position 0.85): slight raised area; sky blue (position 0.9): moderate raised area; dark blue (position 1.0): obvious raised area. A continuous color map containing 256 colors was generated through linear interpolation to ensure smooth and natural color transitions.

[0098] The color values ​​(such as RGB codes) and mapping functions (such as linear or nonlinear interpolation) in this embodiment can be implemented in the following ways: Matlab color mapping code colorPoints = [ % Negative value area (rust, 5 colors) 0.50 0.00 0.50 % Dark Purple (Position 0) 0.90 0.10 0.10 % Rust Red (position 0.15) 1.00 0.50 0.00 % Orange (position 0.35) 1.00 0.80 0.20 % Amber Yellow (position 0.55) 1.00 0.95 0.60 % Light yellow (position 0.75) % Positive value area (raised, 4 colors) 1.00 1.00 1.00 % White (position 0.8) 0.80 0.90 1.00 % Light blue (position 0.85) 0.40 0.75 1.00 % Sky Blue (Position 0.9) 0.00 0.50 0.80 % Dark Blue (Position 1.0) ]; colorPositions = [linspace(0, zeroPosition-0.05, 5), zeroPosition,linspace(zeroPosition+0.05, 1, 3)]; customMap = interp1(colorPositions, colorPoints, linspace(0,1,256)); (2) Visualization of three-dimensional corrosion morphology Create a high-resolution graphics window (1920×1200 pixels) with a white background; X, Y, and Z coordinates correspond to the three-dimensional spatial positions; point sizes are optimized for display effects; color mapping is based on calculated corrosion values; 70% transparency is set to enhance the three-dimensional effect; point edge colors are removed to maintain visual simplicity. The generated 3D cloud map of anchor bolt corrosion morphology is shown below. Figure 9 As shown, Figure 9 The image shows a three-dimensional contour map of anchor bolt corrosion. The X, Y, and Z axes represent the three-dimensional coordinates, and the color bars at the bottom characterize the depth of corrosion. This contour map clearly demonstrates the geometric shape of the anchor bolt in three-dimensional space and the distribution of its corrosion. Figure 9In the diagram, the rust-red areas represent the corroded parts of the anchor bolt, while the light blue areas correspond to the raised crescent-shaped ribs and longitudinal ribs. Combined with the color stripes at the bottom, it can be seen that different colors correspond to the degree of corrosion and rib height information, respectively. As the corrosion depth increases, the color transitions continuously from yellow to red to purplish-red; similarly, the height of the crescent-shaped ribs is also expressed through the shades of color. The purplish-red area represents a corrosion depth of 3.61, which is the maximum corrosion depth of this anchor bolt, and its spatial location is clearly visible in the cloud map. Figure 9 It achieves accurate characterization and three-dimensional reconstruction of the rust morphology of anchor bolts, which can intuitively reflect the overall rust condition and its spatial distribution. It has important engineering significance for scientifically assessing the remaining bearing capacity and predicting the service life of anchor bolts in service. (3) Polar coordinate plane development diagram of rust morphology The anchor bolt is divided into multiple segments along its axial direction (Y-axis) for independent analysis. Point cloud data within each height segment is filtered based on polar coordinate data to maintain the independence and integrity of each segment's data. Polar angles are converted from radians to degrees, maintaining a range of 0-360°. The theoretical perimeter is calculated based on the standard radius: perimeter = 2π × 11.75mm ≈ 73.83mm. Angles are converted to actual arc length: arc length = (angle / 360) × perimeter.

[0099] Planar unfolding generation: Create an independent graphics window and set appropriate dimensions; construct a 3D scatter plot but project it onto a 2D plane; use a top-down perspective (90°) to achieve the planar unfolding effect; maintain a 1:1 aspect ratio to ensure accurate geometric relationships. The generated planar unfolding of anchor bolt corrosion morphology is shown below. Figure 10 As shown, Figure 10 This is a polar coordinate plane development diagram of the anchor bolt corrosion morphology. This diagram is a... Figure 9 The three-dimensional curved surface of the anchor bolt is cut along its longitudinal ribs and unfolded, then mapped onto a two-dimensional plane with the circumferential angle and anchor bolt length as coordinates. Specifically, the circumferential angle is defined as 0–360°, based on the theoretical perimeter calculated from the standard radius of the anchor bolt; the anchor bolt length direction follows the... Figure 9 The Y-axis coordinate is shown in the figure. Colored stripes are also used to represent corrosion depth and crescent rib height. By unfolding the image, the overall distribution of corrosion on the anchor bolt surface can be visually identified. Combined with the color mapping relationship, the degree of corrosion at various locations on the anchor bolt can be further identified.

[0100] The above-described embodiments are merely preferred embodiments provided to fully illustrate the present invention, and the scope of protection of the present invention is not limited thereto. Equivalent substitutions or modifications made by those skilled in the art based on the present invention are all within the scope of protection of the present invention. The scope of protection of the present invention is defined by the claims.

Claims

1. A method for reconstructing a three-dimensional (3D) shape of a bolt corrosion based on a 3D laser scanning, characterized in that: The method comprises the following steps: Step 1: collecting three-dimensional point cloud data of the surface of the anchor rod, and preprocessing the three-dimensional point cloud data; Step 2: performing slice processing on the preprocessed three-dimensional point cloud data, including establishing a local coordinate system aligned with the axial direction of the anchor rod, and performing serialized slicing along the axial direction of the anchor rod at a fixed interval to obtain two-dimensional contour coordinate data of each slice; Step 3: for the two-dimensional contour coordinate data of each slice, a random sample consensus (RANSAC) algorithm with continuity constraint is used to fit a non-corrosion reference circle to obtain the reference center coordinates of each slice; Step 4: based on the reference center coordinates and the nominal radius, the corrosion deviation of each data point on each slice is calculated; Step 5: based on the corrosion deviation, three-dimensional morphology reconstruction and visualization are performed to generate an anchor rod corrosion three-dimensional morphology cloud chart and a polar coordinate development chart.

2. The method of claim 1, wherein the method comprises: The establishment of the local coordinate system comprises: performing plane fitting on the overall point cloud by using the least square method to obtain a plane equation, and establishing a local coordinate system based on the normal vector of the plane equation.

3. The method of claim 1, wherein the method further comprises: The RANSAC algorithm with continuity constraint in step 3 comprises: for the first slice, a standard RANSAC circle fitting is performed; for subsequent slices, the center of the previous slice is taken as a priori position, and a center offset constraint is introduced.

4. The method of claim 3, wherein the method further comprises: The center offset constraint comprises: calculating the Euclidean distance between the current fitting center and the center of the previous circle, and if the distance exceeds a maximum allowed threshold, the center position is corrected.

5. The method of claim 1, wherein: The calculation of the corrosion deviation comprises: for each data point, the Euclidean distance from the data point to the corresponding reference center is calculated, and the nominal radius is subtracted to obtain the corrosion deviation.

6. The method of claim 1, wherein: Step 4 further comprises converting the two-dimensional contour coordinate data of each slice into polar coordinates.

7. The method of claim 1, wherein the method further comprises: The three-dimensional morphology reconstruction and visualization in step 5 comprise: constructing a dynamic color mapping system based on the corrosion deviation to generate a three-dimensional morphology cloud chart and a polar coordinate development chart; The dynamic color mapping system uses a bidirectional color mapping, wherein a negative value area represents a corrosion area and uses a gradient color from deep purple to light yellow, and a positive value area represents a protrusion area and uses a gradient color from white to deep blue.

8. A three-dimensional reconstruction system for the corrosion morphology of an anchor rod based on three-dimensional laser scanning, characterized by: The method comprises a data acquisition module, a preprocessing module, a slice processing module, a contour reconstruction module, a quantitative calculation module, and a visualization module; The data acquisition module is configured to acquire three-dimensional point cloud data of the surface of the anchor rod; The preprocessing module is configured to preprocess the three-dimensional point cloud data; The slice processing module is configured to perform slice processing on the preprocessed three-dimensional point cloud data, including establishing a local coordinate system aligned with the axial direction of the anchor rod, and performing serialized slicing along the axial direction of the anchor rod at a fixed interval to obtain two-dimensional contour coordinate data of each slice; The contour reconstruction module is configured to, for the two-dimensional contour coordinate data of each slice, use a random sample consensus (RANSAC) algorithm with continuity constraint to fit a non-corrosion reference circle to obtain the reference center coordinates of each slice; The quantitative calculation module is configured to calculate the corrosion deviation of each data point on each slice based on the reference center coordinates and the nominal radius; The three-dimensional morphology reconstruction and visualization in step 5 comprise: constructing a dynamic color mapping system based on the corrosion deviation to generate a three-dimensional morphology cloud chart and a polar coordinate development chart; The visualization module is configured to perform three-dimensional topography reconstruction and visualization based on the corrosion deviation amount, and generate an anchor rod corrosion three-dimensional topography cloud chart and a polar coordinate development chart.

9. The three-dimensional laser scanning based anchor rod corrosion topography three-dimensional reconstruction system according to claim 8, wherein: The slice processing module comprises a plane fitting unit configured to perform plane fitting on the overall point cloud by using a least square method to obtain a plane equation, and establish a local coordinate system based on a normal vector of the plane equation.

10. The three-dimensional laser scanning based anchor rod corrosion topography three-dimensional reconstruction system according to claim 8, wherein: The contour reconstruction module is configured to, for a first slice, perform standard RANSAC circle fitting; for a subsequent slice, take a center of a previous slice as a position prior, and introduce a circle center offset constraint; calculate an Euclidean distance between a current fitting circle center and a circle center of a previous slice, and if the Euclidean distance exceeds a maximum allowable threshold, correct the circle center position.

Images