Drill pipe thickening transition zone quality detection method and system based on inner conical surface scanning

Abstract

The application discloses a drill pipe thickening transition zone quality detection method and system based on inner conical surface scanning, and particularly relates to the field of precision measurement of complex inner surface geometric appearance, and is used for solving the problem that the prior art cannot accurately and comprehensively evaluate the quality of the inner complex conical surface of the drill pipe thickening transition zone; the scanning device is placed in the inner hole of the drill pipe to scan to obtain complete point cloud data of the inner conical surface; the point cloud data is processed to generate an inner conical surface three-dimensional data set; micro geometric shape features are calculated based on the data set, and a plurality of functional evaluation sub-regions are divided according to the features; signal transformation analysis is performed on the local three-dimensional appearance data of each sub-region to obtain a signal domain feature data set; a feature parameter set representing the surface quality state is extracted from the signal domain feature data set; the parameter set of each sub-region is compared with a preset reference, and it is comprehensively judged whether the quality is qualified; the fine quantitative evaluation of the geometric quality of the inner conical surface of the drill pipe is realized, and the pertinence and reliability of the detection are improved.

Description

Technical Field

[0001] This invention relates to the field of precision measurement technology for complex internal surface geometries, and more specifically, to a method and system for quality inspection of the thickened transition zone of drill pipe based on internal conical surface scanning. Background Technology

[0002] The thickened transition zone of the drill pipe is a key structure connecting the thick-walled joint and the thin-walled tube body. Its geometry and surface quality directly affect the stress distribution and fatigue life of the drill pipe under complex downhole loads. In the field of drill pipe manufacturing and quality inspection, existing technologies typically focus on detecting the drill pipe's outer diameter, straightness, thread accuracy, or macroscopic surface defects, such as using calipers, straightness measuring instruments, thread gauges, or magnetic particle testing. However, for the conical surface within the thickened transition zone—a complex curved surface located inside the drill pipe—due to its poor spatial accessibility and unique geometric characteristics, conventional contact measurements or external scanning are insufficient to obtain complete and accurate three-dimensional morphological data.

[0003] Existing technologies lack effective means to accurately and comprehensively detect the geometric quality of the conical surface within the thickened transition zone of the drill pipe. This makes it impossible to establish a quantitative evaluation benchmark for the shape accuracy and surface continuity of this critical area. Consequently, the quality control of the thickened transition zone of the drill pipe largely relies on empirical judgment and indirect parameters, making it difficult to accurately identify and quantify geometric defects in the transition zone, such as uneven transition, inconsistent taper, and local depressions. This poses a potential risk of early fatigue failure of the drill pipe due to uncontrolled quality in the transition zone. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides a method and system for quality inspection of the thickened transition zone of drill pipe based on internal conical surface scanning to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] The quality inspection method for the thickened transition zone of drill pipe based on internal conical surface scanning includes the following steps:

[0007] S1. Insert the detection part of the scanning device into the inner hole of the drill rod, drive the scanning device to move along the axial direction of the drill rod, and simultaneously scan the inner conical surface of the thickened transition zone of the drill rod to obtain the point cloud data of the inner conical surface;

[0008] S2. Process the point cloud data to obtain a three-dimensional dataset of the inner cone surface that represents the complete morphology of the inner cone surface;

[0009] S3. Calculate the micro-geometric features of each local region of the inner cone surface based on the three-dimensional dataset of the inner cone surface, and divide the three-dimensional dataset of the inner cone surface into multiple functional evaluation sub-regions according to the distribution of the micro-geometric features.

[0010] S4. Perform signal transformation analysis on the local three-dimensional topography data corresponding to the functional evaluation sub-region in the three-dimensional dataset of the inner cone surface to obtain the signal domain feature dataset corresponding to the functional evaluation sub-region.

[0011] S5. Extract the set of feature parameters from each signal domain feature dataset to quantify the surface quality status of the functional evaluation sub-region.

[0012] S6. Compare the feature parameter set with the preset benchmark parameter range for each functional evaluation sub-region to determine whether the quality of the drill pipe thickening transition zone is qualified.

[0013] Furthermore, the probe portion of the scanning device is inserted into the inner hole of the drill pipe, and the scanning device is driven to move along the axial direction of the drill pipe, simultaneously scanning the inner conical surface of the thickened transition zone of the drill pipe to acquire point cloud data of the inner conical surface, including:

[0014] The drive scanning device moves its detection part along the axial direction of the drill pipe's inner hole;

[0015] During the movement, the control and detection section scans the inner cone surface along its generatrix direction;

[0016] The spatial coordinates of the inner cone surface points collected by the detection part at each axial position and circumferential angle are recorded simultaneously to form the point cloud data of the inner cone surface.

[0017] Further, the point cloud data is processed to obtain a 3D dataset of the inner cone surface representing its complete morphology, including:

[0018] Based on the relative positional relationship of spatial coordinates in point cloud data with an inner cone surface, coordinate transformation and alignment are performed on the point cloud data.

[0019] Based on the aligned point cloud data, a continuous inner cone surface model is constructed.

[0020] Based on the inner cone surface model, a three-dimensional dataset of the inner cone surface is generated, containing the three-dimensional coordinates and topological connections of each point on the inner cone surface.

[0021] Furthermore, based on the three-dimensional dataset of the inner cone surface, the microscopic geometric features of each local region of the inner cone surface are calculated, and the three-dimensional dataset of the inner cone surface is divided into multiple functional evaluation sub-regions according to the distribution of the microscopic geometric features, including:

[0022] Define multiple local analysis windows on the inner cone surface 3D dataset;

[0023] Within each local analysis window, the complexity feature quantity representing the surface irregularity is calculated based on the spatial distribution of the three-dimensional coordinate points within the window, and the directional feature quantity representing the consistency of the surface texture direction is also calculated.

[0024] The complexity and directional features of each local analysis window are combined to form a feature vector that characterizes the micro-geomorphology of each local region.

[0025] Based on the aggregation relationship of the feature vectors of all local analysis windows in the feature space, the inner cone surface 3D dataset is divided into multiple functional evaluation sub-regions.

[0026] Furthermore, based on the aggregation relationship of the feature vectors of all local analysis windows in the feature space, the inner cone 3D dataset is divided into multiple functional evaluation sub-regions, including:

[0027] By analyzing the similarity measure between feature vectors, a set of data points with high cohesion in the feature space is identified;

[0028] Map each set of highly cohesive data points back to the corresponding spatial region in the inner cone 3D dataset;

[0029] Each mapped spatial region is defined as a functional evaluation sub-region.

[0030] Furthermore, signal transformation analysis was performed on the local three-dimensional topography data corresponding to the functional evaluation sub-region in the three-dimensional dataset of the inner cone surface to obtain the signal domain feature dataset corresponding to the functional evaluation sub-region, including:

[0031] For each functional evaluation sub-region, extract the contour height sequence along the generatrix of the inner cone surface from the corresponding local 3D topography data;

[0032] Spectral analysis of the contour height sequence yields spectral characteristics that characterize the frequency and energy distribution of contour undulations.

[0033] Based on spectral characteristics, a signal domain feature dataset is constructed to describe the periodic or non-periodic fluctuations in the surface morphology of the functional evaluation sub-region.

[0034] Furthermore, spectral analysis of the contour height sequence yields spectral characteristics that characterize the frequency and energy distribution of contour undulations, including:

[0035] The contour height sequence is converted into a frequency domain representation to obtain the frequency spectrum;

[0036] Identify characteristic frequency components in the frequency spectrum whose energy exceeds a preset threshold;

[0037] Based on the characteristic frequency components and their corresponding energies, spectral features are constructed.

[0038] Furthermore, a set of feature parameters for quantifying the surface quality state of the functional evaluation sub-region is extracted from each signal domain feature dataset, including:

[0039] Read spectral features from the signal domain feature dataset;

[0040] The parameters representing the energy concentration of the surface profile are calculated based on the spectral characteristics; the parameters representing the harmonic component energy ratios are calculated based on the spectral characteristics.

[0041] The main frequency band energy ratio parameter and the harmonic component energy ratio parameter are combined into a characteristic parameter set.

[0042] Furthermore, the feature parameter set is compared with the preset benchmark parameter range for each functional evaluation sub-region to determine whether the quality of the drill pipe thickening transition zone is qualified, including:

[0043] For each functional evaluation sub-region, each parameter in the feature parameter set of the functional evaluation sub-region is compared one by one with the corresponding parameter range in the preset benchmark parameter range for the corresponding functional evaluation sub-region, and the parameter compliance status of the corresponding functional evaluation sub-region is generated.

[0044] Based on the parameter compliance status of all functional evaluation sub-regions and combined with the preset quality influence weights of each functional evaluation sub-region, it is determined whether the overall quality status meets the qualification conditions, thereby determining whether the quality of the drill pipe thickening transition zone is qualified.

[0045] On the other hand, the present invention provides a drill pipe thickening transition zone quality inspection system based on internal conical surface scanning, comprising the following modules:

[0046] The point cloud acquisition module is used to insert the detection part of the scanning device into the inner hole of the drill pipe, drive the scanning device to move along the drill pipe axis, and simultaneously scan the inner conical surface of the thickened transition zone of the drill pipe to acquire the point cloud data of the inner conical surface.

[0047] The 3D modeling module is used to process point cloud data and obtain a 3D dataset of the inner cone surface that represents the complete morphology of the inner cone surface.

[0048] The partitioning module is used to calculate the micro-geometric features of each local region of the inner cone surface based on the three-dimensional dataset of the inner cone surface, and to divide the three-dimensional dataset of the inner cone surface into multiple functional evaluation sub-regions according to the distribution of the micro-geometric features.

[0049] The signal transformation module is used to perform signal transformation analysis on the local three-dimensional topography data corresponding to the functional evaluation sub-region in the three-dimensional dataset of the inner cone surface, and to obtain the signal domain feature dataset corresponding to the functional evaluation sub-region.

[0050] The parameter extraction module is used to extract the set of feature parameters from each signal domain feature dataset to quantify the surface quality status of the sub-region of the functional evaluation function.

[0051] The quality assessment module is used to compare the set of feature parameters with the preset benchmark parameter range for each functional evaluation sub-region to determine whether the quality of the thickened transition zone of the drill pipe is qualified.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] 1. By scanning the conical surface within the thickened transition zone of the drill pipe and acquiring complete three-dimensional morphological data, a precise digital characterization of the geometric quality of this hidden and complex curved surface is achieved. Unlike indirect assessments that rely on external dimensions or macroscopic defects, this method can directly obtain quantitative data on the microscopic geometric morphology of the inner conical surface, thereby establishing an objective evaluation benchmark based on measured morphology. By intelligently dividing the overall inner conical surface into multiple functional evaluation sub-regions according to the distribution of microscopic geometric features, it is recognized that different regions may assume different mechanical roles or have different failure sensitivities during service. This achieves a leap from overall general evaluation to refined correlation evaluation of local features, making the quality assessment closer to the actual functional requirements of the structure.

[0054] 2. By performing signal transformation analysis on the morphological data of each functional sub-region, the analysis moves from traditional spatial geometric parameter evaluation to frequency domain feature analysis. This effectively captures and quantifies deeper quality information such as the periodicity, regularity, and energy distribution concentration of surface texture. This parameter set based on signal domain feature extraction provides richer and more sensitive multi-dimensional quantitative indicators for surface quality status. By comparing the quantitative parameters of each sub-region with its preset benchmark range and combining them with regional importance weights for comprehensive judgment, a multi-level, weighted fusion decision-making mechanism is formed. This ensures that quality judgment is not only based on whether it exceeds the standard, but also considers the location of defects and their potential impact, thereby significantly improving the scientificity and reliability of the detection results. This provides a direct and effective precise measurement basis for quality control and life prediction of the thickened transition zone of the drill pipe. Attached Figure Description

[0055] Figure 1 This is a flowchart of the drill pipe thickening transition zone quality inspection method based on inner conical surface scanning according to the present invention;

[0056] Figure 2 This is a schematic diagram of the quality inspection system for the thickened transition zone of drill pipe based on internal conical surface scanning according to the present invention. Detailed Implementation

[0057] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0058] Example 1: Figure 1 The present invention provides a method for quality inspection of the thickened transition zone of drill pipe based on internal conical surface scanning, which includes the following steps:

[0059] S1. Insert the detection part of the scanning device into the inner hole of the drill rod, drive the scanning device to move along the axial direction of the drill rod, and simultaneously scan the inner conical surface of the thickened transition zone of the drill rod to obtain the point cloud data of the inner conical surface;

[0060] S2. Process the point cloud data to obtain a three-dimensional dataset of the inner cone surface that represents the complete morphology of the inner cone surface;

[0061] S3. Calculate the micro-geometric features of each local region of the inner cone surface based on the three-dimensional dataset of the inner cone surface, and divide the three-dimensional dataset of the inner cone surface into multiple functional evaluation sub-regions according to the distribution of the micro-geometric features.

[0062] S4. Perform signal transformation analysis on the local three-dimensional topography data corresponding to the functional evaluation sub-region in the three-dimensional dataset of the inner cone surface to obtain the signal domain feature dataset corresponding to the functional evaluation sub-region.

[0063] S5. Extract the set of feature parameters from each signal domain feature dataset to quantify the surface quality status of the functional evaluation sub-region.

[0064] S6. Compare the feature parameter set with the preset benchmark parameter range for each functional evaluation sub-region to determine whether the quality of the drill pipe thickening transition zone is qualified.

[0065] S1. Insert the probe of the scanning device into the inner hole of the drill pipe, drive the scanning device to move along the axial direction of the drill pipe, and simultaneously scan the inner conical surface of the thickened transition zone of the drill pipe to obtain the point cloud data of the inner conical surface. The specific implementation is as follows:

[0066] In step S1, the probe portion of the scanning device is inserted into the inner hole of the drill rod through the opening at the end of the drill rod, and positioned at one end of the conical surface region within the thickened transition zone to be inspected. Driving the scanning device to move its probe portion along the axial direction of the drill rod's inner hole is achieved through a linear drive mechanism. This linear drive mechanism includes a precision lead screw driven by a servo motor and a slider that engages with the lead screw and is rigidly connected to the probe portion of the scanning device. When the servo motor rotates according to control commands, the rotational motion is converted into linear motion of the slider and the probe portion of the scanning device along the drill rod's axial direction via the precision lead screw transmission. To ensure smooth movement and accurate positioning, the servo motor's operation is controlled in a closed-loop manner by a motion control card. The motion control card receives feedback signals from a high-precision rotary encoder mounted at the end of the precision lead screw and adjusts the servo motor's speed and direction in real time. The movement speed of the probe portion along the axis of the scanning device is set within the range of 0.5 mm / s to 5 mm / s. The specific speed value depends on the required point cloud density and the complexity of the inner conical surface geometry; for example, a lower movement speed of 1 mm / s can be used for sections requiring higher resolution inspection. The entire axial movement travels from the beginning to the end of the conical surface within the thickened transition zone.

[0067] As the probe section of the scanning device moves along the axial direction, it scans the inner conical surface along its generatrix. This scanning action is accomplished by an optical measurement component integrated into the probe section. This optical measurement component, based on the laser triangulation principle, includes a laser emitter and an image sensor. The laser emitter emits a fan-shaped laser beam, which is projected onto the inner conical surface to form a bright laser stripe. The image sensor is a charge-coupled device (CCD) array whose optical axis forms a fixed angle with the output optical axis of the laser emitter, used to capture the diffuse reflection image of the laser stripe on the inner conical surface. To achieve scanning along the generatrix direction, the length direction of the laser beam is pre-adjusted to be substantially parallel to the generatrix direction of the inner conical surface. At each instant the probe section of the scanning device moves axially, the laser stripe covers a complete generatrix region of the inner conical surface at that axial position. The image sensor continuously acquires two-dimensional images containing the laser stripe at a fixed frame rate. To obtain the morphology of the entire circumference of the inner conical surface, the scanning process needs to cover all circumferential angles; this is achieved by controlling the probe part of the scanning device to rotate slowly and continuously around the axis of the drill rod while the probe part moves axially. The rotational motion is driven by another miniature servo motor and transmitted to the housing of the probe part of the scanning device through a reduction gear set. The rotational speed is matched to the axial movement speed to ensure that the helical scanning path covers the entire inner conical surface without omission; for example, when the axial movement speed is 2 mm per second, the rotational speed can be set to 15 degrees per second.

[0068] The spatial coordinates of points on the inner conical surface acquired by the scanning device's probe at each axial position and circumferential angle are synchronously recorded, forming point cloud data of the inner conical surface. Synchronous recording is achieved through a data acquisition and processing unit, which is electrically connected to a motion control card, a high-precision rotary encoder, and an image sensor. For each acquired two-dimensional image, the data acquisition and processing unit first performs image processing, using a laser stripe center extraction algorithm to accurately extract the two-dimensional coordinates of each pixel on the laser stripe center line from the image. Simultaneously, the data acquisition and processing unit reads the precise axial position of the scanning device's probe in millimeters from the motion control card in real time. The data acquisition and processing unit also reads the current circumferential angle value in degrees from the encoder driving the rotating micro servo motor. Synchronization between image acquisition and position / angle reading is achieved through hardware triggering, ensuring that the two-dimensional coordinates of each extracted laser point can be associated with a unique axial position and circumferential angle. Subsequently, based on the pre-calibrated laser triangulation system parameters, the data acquisition and processing unit transforms the two-dimensional image coordinates of each laser point, along with its corresponding axial position and circumferential angle, into three-dimensional spatial coordinates within the same three-dimensional coordinate system fixed to the scanning device base through triangulation calculations. The calibration process for the laser triangulation system parameters includes: using a standard block with known three-dimensional dimensions, placing the standard block at different positions within the measurement range and acquiring laser stripe images; and determining the spatial equation of the laser plane and the intrinsic and extrinsic parameters of the image sensor by solving a series of equations. All calculated three-dimensional spatial coordinate points are stored in an orderly manner, forming a structured point cloud dataset of the inner cone surface. In the point cloud dataset of the inner cone surface, each data point contains three-dimensional coordinate information and its corresponding axial position information.

[0069] S2. Process the point cloud data to obtain a 3D dataset representing the complete morphology of the inner cone surface. The specific implementation is as follows:

[0070] Based on the relative spatial coordinates in the point cloud data of the inner conical surface, coordinate transformation and alignment are performed on the point cloud data. Since the scanning device's probe moves along the drill pipe axis and may rotate during scanning, each 3D spatial coordinate point in the acquired point cloud data of the inner conical surface is initially calculated in a local coordinate system relative to the scanning device's probe itself, which changes with the probe's movement. To obtain a complete and uniform conical surface morphology within the thickened transition zone, these point cloud fragments acquired from different instantaneous poses must be transformed and unified into a single global coordinate system. This process is coordinate transformation and alignment. In practice, a fixed global coordinate system is defined, for example, setting the origin of the global coordinate system at the center of the drill pipe end face, with the Z-axis coinciding with the drill pipe axis. The core of coordinate transformation and alignment is to solve for the transformation matrix of the scanning device's probe's local coordinate system relative to this global coordinate system at each acquisition instant. One method to solve for the transformation matrix is ​​to utilize position feedback data during the scanning process. For a point acquired at a certain moment, the transformation relationship of its local coordinate system relative to the starting position can be obtained by accumulating the axial displacement and rotation angle recorded from the high-precision rotary encoder. Combined with the preset calibration relationship between the local coordinate system and the global coordinate system at the starting position, the transformation matrix from the local coordinate system to the global coordinate system corresponding to that point can be calculated. Another method is based on registration of the point cloud data itself. Two continuously acquired point cloud datasets with partially overlapping areas are used as input, and the optimal spatial transformation between them is calculated using an iterative nearest-point algorithm. The steps of the iterative nearest-point algorithm include: for each point in the first point cloud, finding the point in the second point cloud with the closest Euclidean distance as its corresponding point; then calculating a rotation matrix and translation vector such that after this transformation, the sum of the squared average distances between all points in the first point cloud and their corresponding points in the second point cloud is minimized; then updating the coordinates of all points in the first point cloud with this calculated transformation; repeating the process of finding corresponding points and calculating the optimal transformation until the maximum number of iterations is reached, for example, 100 times. Regardless of the method used, the corresponding transformation matrix is ​​applied to the point cloud fragment obtained at each acquisition moment to uniformly transform the coordinates of all points to the global coordinate system, resulting in a complete point cloud data set of the inner cone surface that is accurately aligned in space.

[0071] Based on aligned point cloud data, a continuous inner cone surface model is constructed. The aligned inner cone point cloud data is a collection of discrete 3D spatial points. To facilitate subsequent geometric analysis and calculations, a continuous surface model capable of representing the true shape of the inner cone needs to be reconstructed from these discrete points. One method for constructing a continuous surface model is triangulation. Triangulation processes the 3D coordinate points in the aligned point cloud data. The process first involves triangulating these spatial points, that is, connecting adjacent spatial points with triangular facets under certain optimization criteria to form a continuous mesh surface composed of numerous triangular facets. A specific triangulation process is as follows: first, the 3D point cloud is projected onto a 2D plane along its principal direction, for example, onto the XY plane perpendicular to the Z-axis of the global coordinate system; triangulation is performed on the 2D projection points to generate a set of 2D triangles; then, the vertex index relationships of these 2D triangles are mapped back to the original 3D spatial points, thus forming a triangular mesh in 3D space. Since point cloud data may contain noise, direct triangulation may produce connections that do not conform to the true surface. Therefore, surface filtering and optimization can be performed after triangular meshing. For example, the dihedral angle between each triangular facet and its adjacent facets can be calculated. If the dihedral angle is greater than a preset angle threshold, such as 150 degrees, it is considered that there may be abnormal connections at that point, and the mesh connection relationship in that region is adjusted or smoothed. The constructed inner conical surface model is a triangular mesh composed of a vertex list and a triangular facet index list. The vertex list consists of the 3D coordinate points in the aligned point cloud data, and the triangular facet index list defines how every three vertices are connected to form a triangular facet, thus approximating the continuous inner conical surface in a piecewise linear manner.

[0072] The preset angle threshold is set to 150 degrees, mainly based on the rationality of the spatial topological relationship of the mesh triangular facets. When the dihedral angle between adjacent triangular facets is close to 180 degrees, it usually means that the surface is too flat or there is abnormal overlap. Setting the threshold to 150 degrees can effectively identify and filter out such sharp connection features that should not appear on smooth continuous surfaces, thereby optimizing mesh quality while maintaining realistic geometric details.

[0073] Based on the inner conical surface model, a 3D dataset of the inner conical surface is generated, containing the 3D coordinates and topological connections of each point on the inner conical surface. The inner conical surface 3D dataset contains at least two core parts. The first part is a vertex coordinate array, directly derived from the vertex list of the inner conical surface model, storing the ordered X, Y, and Z coordinate values ​​of each vertex in millimeters. The second part is a patch index array, derived from the triangular patch index list of the inner conical surface model, storing the position indices of the three vertices corresponding to each triangular patch in the vertex coordinate array. Each group of three indices in the patch index array defines which three vertices are connected together to form a minimum surface unit. All triangular patches share vertices and are seamlessly connected, collectively representing the complete morphology of the inner conical surface. In addition to vertex and patch information, the inner conical surface 3D dataset can also contain other auxiliary information. For example, the normal vector information of each vertex can be calculated and stored; the normal vector of a vertex can be obtained by calculating the average of the unit normal vectors of all triangular patches sharing that vertex. Normal vector information is an important input for subsequent calculations of microscopic geometric features such as local curvature. The generation of the inner cone surface 3D dataset means that the aligned discrete point cloud is transformed into a digital surface model with a clear geometric definition and topological structure. This model fully represents the 3D morphology of the inner cone surface, providing an accurate and structured data foundation for subsequent functional evaluation sub-region division and signal transformation analysis.

[0074] S3. Calculate the microscopic geometric features of each local region of the inner cone surface based on the three-dimensional dataset of the inner cone surface, and divide the three-dimensional dataset of the inner cone surface into multiple functional evaluation sub-regions according to the distribution of the microscopic geometric features. The specific implementation is as follows:

[0075] Multiple local analysis windows are defined on the inner conical surface 3D dataset. In practice, a local analysis window is a sampling region defined on the digital surface model represented by the inner conical surface 3D dataset, used for calculating local geometric features. One method for defining local analysis windows is to perform uniform meshing on the parameter domain of the inner conical surface 3D dataset. Since the inner conical surface is approximately a surface of revolution, it can be unfolded onto a two-dimensional parametric plane, where one parameter is the axial position along the drill pipe axis and the other is the circumferential angle around the axis. Multiple regular rectangular meshes are divided on this two-dimensional parametric plane; for example, the axial range is divided into 30 equal segments, and the circumferential 360 degrees are divided into 36 equal parts. Each rectangular mesh corresponds to a local analysis window on the inner conical surface 3D dataset surface. The size of the local analysis window needs to be set according to the scale of the microscopic geometric features to be analyzed; for example, the axial size of the window can be set to 5 mm, and the circumferential size can be set to 10 degrees to ensure that the window contains a sufficient number of 3D coordinate points for stable feature calculation, while preventing the local details from being blurred due to an excessively large window. Another method for defining local analysis windows is to perform sliding window sampling based on the triangular mesh structure of the inner cone 3D dataset. Starting from the center of a triangular facet in the dataset, all vertices within a 3mm Euclidean distance of that facet are searched. The region containing these vertices is defined as a local analysis window. Then, the center point of this window is moved in 1mm increments to cover the entire inner cone. Regardless of the method used, dozens to hundreds of potentially overlapping local analysis windows are defined on the inner cone 3D dataset.

[0076] Within each local analysis window, complexity features characterizing surface irregularity are calculated based on the spatial distribution of 3D coordinate points within the window, and directional features characterizing the consistency of surface texture orientation are also calculated. For each defined local analysis window, the input data consists of all 3D coordinate points within the window's coverage area, derived from the vertex coordinate array of the inner cone 3D dataset. A specific method for calculating the complexity features characterizing surface irregularity is to calculate the root mean square (RMS) value of the distance from all 3D coordinate points within the window to a best-fit plane. First, a plane is fitted using the least squares method to minimize the sum of the squares of the perpendicular distances from all 3D coordinate points within the window to this plane. Then, the perpendicular distance from each 3D coordinate point to this best-fit plane is calculated. Finally, the RMS value of all these distances is calculated; this RMS value serves as the complexity feature. The RMS value is measured in millimeters; a larger value indicates more pronounced surface undulations relative to the ideal plane within the window, and thus a more irregular surface. A specific method for calculating the directional features characterizing the consistency of surface texture orientation is based on analyzing the normal vector distribution of the 3D coordinate points within the window. First, the normal vector of each 3D coordinate point within the window needs to be obtained. This normal vector information can be directly read from the auxiliary information of the inner cone 3D dataset. If not stored, it can be calculated by weighted averaging the normal vectors of the triangular facet containing that point. Then, the normal vectors of all points within the window are projected onto a 2D plane parallel to the local tangent plane of the local analysis window, resulting in a series of 2D direction vectors. Next, the direction angles of these 2D direction vectors are calculated, and a histogram of the direction angle distribution within the range of 0 to 360 degrees is plotted. The 360 ​​degrees are divided into 36 intervals, each 10 degrees. The directional characteristic can be characterized by calculating the entropy value of this direction angle distribution histogram. The entropy value is calculated by multiplying the negative probability of each interval by the base-2 logarithm of that probability. Entropy is dimensionless; the lower the value, the more concentrated the direction angle distribution and the stronger the directional consistency of the surface texture. The higher the entropy value, the more dispersed the direction angle distribution and the more chaotic and directionless the surface texture. Each local analysis window outputs two values: a complexity feature in millimeters and a dimensionless directional feature.

[0077] The calculated complexity and directional features of each local analysis window are combined to form a feature vector representing the micro-geometry of each local region. For the i-th local analysis window, assume the calculated complexity feature is Ci and the directional feature is Di. These two values ​​are combined sequentially into a two-dimensional vector [Ci, Di]. This two-dimensional vector is the feature vector representing the micro-geometry of the local region corresponding to this local analysis window. If more features are used, such as adding surface curvature features, the dimension of the feature vector will increase accordingly, for example, forming a three-dimensional vector [Ci, Di, Ki], where Ki is the curvature feature. The values ​​of each dimension of the feature vector have different dimensions and orders of magnitude; in order to avoid a certain dimension dominating the distance calculation due to its large value in subsequent cluster analysis, it is usually necessary to normalize the feature vector. One method of normalization is linear normalization, which processes the same feature for all local analysis windows separately, mapping the maximum value of the feature to 1, the minimum value to 0, and the intermediate values ​​linearly scaled. After combination and normalization, each local analysis window on the inner cone surface 3D dataset corresponds to a point in the multidimensional feature space, i.e., its feature vector.

[0078] Based on the clustering relationships of the feature vectors of all local analysis windows in the feature space, the 3D dataset of the inner cone surface is divided into multiple functional evaluation sub-regions. This process essentially classifies or clusters the local analysis windows based on the similarity of their micro-geometric features, grouping windows with similar features and proximity in the feature space into the same group. Each group, after being mapped back to 3D space, forms a continuous region, i.e., a functional evaluation sub-region. By analyzing the similarity measure between feature vectors, sets of data points with high cohesion in the feature space are identified. The similarity measure uses Euclidean distance; the smaller the Euclidean distance between two feature vectors, the more similar the micro-geometry of the two local analysis windows they represent. A specific method for identifying sets of highly cohesive data points is to use the K-means clustering algorithm. The implementation steps of the K-means clustering algorithm are as follows: First, randomly select K feature vectors as initial cluster centers. The value of K is the number of clusters that needs to be preset. The number of clusters K can be set by combining the elbow rule with specific application experience, for example, K=3. Then, for each feature vector, calculate its Euclidean distance to all K cluster centers and assign it to the cluster to which the nearest cluster center belongs. Next, for each cluster, recalculate the mean of all feature vectors in that cluster and use this new mean vector as the new cluster center for that cluster. Repeat the steps of assigning feature vectors and updating cluster centers until the position change of all cluster centers is less than 0.01, or the preset maximum number of iterations is reached, such as 200. All feature vectors are divided into K sets. Feature vectors within each set are close to each other by Euclidean distance, exhibiting high cohesion, while feature vectors in different sets are far apart by Euclidean distance. Map each set of highly cohesive data points back to the corresponding spatial region in the inner cone 3D dataset. Since each feature vector corresponds to a local analysis window, and each local analysis window corresponds to a spatial location on the inner cone 3D dataset, the union of the spatial locations covered by all feature vectors belonging to the same cluster and their corresponding local analysis windows on the inner cone 3D dataset constitutes a spatial region. Each mapped spatial region is defined as a functional evaluation sub-region. Finally, each vertex or triangular facet in the inner cone 3D dataset is labeled to indicate which functional evaluation sub-region it belongs to, thus completing the partitioning of the entire inner cone 3D dataset. For example, it might be divided into three functional evaluation sub-regions: one with high complexity and low directional consistency, one with low complexity and high directional consistency, and one with medium complexity and medium directional consistency. Each functional evaluation sub-region will serve as the basic unit for subsequent independent signal transformation analysis and evaluation.

[0079] S4. Perform signal transformation analysis on the local three-dimensional topography data corresponding to the functional evaluation sub-region in the three-dimensional dataset of the inner cone surface to obtain the signal domain feature dataset corresponding to the functional evaluation sub-region. The specific implementation is as follows:

[0080] For each functional evaluation sub-region, the contour height sequence along the inner cone surface generatrix direction is extracted from the corresponding local 3D topography data. Specifically, for each defined functional evaluation sub-region, the corresponding local 3D topography data (i.e., the inner cone surface 3D dataset) contains the coordinates and topological connections of all vertices belonging to that functional evaluation sub-region. The first step in extracting the contour height sequence is to define a series of sampling generatrixes on the digital surface model of the functional evaluation sub-region. One method for defining the sampling generatrixes is to first determine the centerline of the functional evaluation sub-region along the drill pipe axis, and then set a series of sampling points along this centerline at fixed axial intervals. The axial interval is set based on the minimum period of surface undulations to be captured; for example, to analyze surface undulations with spatial frequencies up to 2 periods per millimeter, according to the Nyquist sampling theorem, the sampling interval should be less than 0.25 millimeters, so a sampling interval of 0.1 millimeters can be set. A cross-sectional plane perpendicular to the drill pipe axis is drawn through each sampling point. This cross-sectional plane intersects the digital surface model of the functional evaluation sub-region, resulting in a spatial intersection line. On this spatial intersection line, a specific circumferential starting point is selected, such as the point closest to the keyway direction of the drill pipe. Starting from this starting point, points are taken at fixed angular intervals along the spatial intersection line. The angular interval determines the circumferential resolution; for example, if the angular interval is set to 1 degree, 360 points are taken on one generatrix. For each taken point, the radial distance to a straight line passing through the sampling point and parallel to the drill pipe axis is calculated. This radial distance is defined as the profile height value of that point on the current sampling generatrix, in millimeters. Along a fixed circumferential direction, the profile height values ​​of all points taken sequentially on the same cross-section are recorded, forming a discrete profile height sequence along that sampling generatrix. Then, the process is repeated at the sampling point at the next axial position to obtain another profile height sequence for the sampling generatrix. For a functional evaluation sub-region, multiple profile height sequences can be obtained. Each profile height sequence contains the same number of discrete profile height values, equal to the number of points sampled circumferentially. All these contour height sequences are arranged in axial order to form a set of contour height sequences in the form of a two-dimensional matrix.

[0081] Spectral analysis is performed on the contour height sequence to obtain spectral characteristics representing the frequency and energy distribution of contour undulations. The object of spectral analysis is each contour height sequence extracted in the previous step. Since the contour height sequence is a discrete digital signal, the spectral analysis uses Discrete Fourier Transform (DFT). First, each contour height sequence is preprocessed; preprocessing includes removing trend terms to eliminate the influence of the overall tilt caused by the macroscopic taper of the inner cone on the high-frequency fluctuation analysis. One method for removing trend terms is to calculate the linear least squares fitted line of the contour height sequence, and then subtract the contour height value corresponding to this fitted line from the original contour height values ​​to obtain a zero-mean contour height sequence. Next, a windowing function, such as a Hanning window, is applied to the zero-mean contour height sequence to reduce spectral leakage. Then, a Fast Fourier Transform (FFT) is performed on the windowed sequence. The number of points in the FFT is usually set to an integer power of 2 greater than or equal to the sequence length; for example, if the sequence length is 360, the number of points in the FFT can be set to 512. After performing the FFT, the frequency domain representation of the contour height sequence in complex form is obtained. The frequency spectrum is obtained by calculating the square of the modulus of a complex sequence, reflecting the distribution of signal energy across different frequency components. The horizontal axis of the frequency spectrum represents spatial frequency, measured in cycles per millimeter; digital frequencies need to be converted to physical spatial frequencies based on the actual spatial sampling interval. The contour height sequence is converted to a frequency domain representation to obtain the frequency spectrum; characteristic frequency components with energy exceeding a preset threshold are identified from the frequency spectrum. The preset threshold is a critical value used to determine whether a frequency component has significant energy. One method for setting the preset threshold is based on the background noise level of the frequency spectrum. First, the high-frequency range that mainly contains noise in the frequency spectrum is determined, for example, frequencies higher than one cycle per millimeter; the average energy of all frequency points within this high-frequency range is calculated as an estimate of the background noise level. Then, this estimated background noise level is multiplied by a coefficient to obtain the preset threshold, which can be empirically set between 3 and 10, for example, a coefficient of 5. After determining the preset threshold, each frequency point in the frequency spectrum is traversed, and frequency points with energy values ​​exceeding the preset threshold are marked as characteristic frequency components. For each identified characteristic frequency component, its frequency value and corresponding energy value are recorded. Based on characteristic frequency components and their corresponding energies, spectral features are constructed. For a functional assessment sub-region, statistical analysis of the spectra of all contour height sequences within the region is required to obtain representative features. For example, the average energy value of the frequency spectrum of all contour height sequences at each frequency point is calculated to form an average frequency spectrum. Simultaneously, the occurrence frequency and average energy of the characteristic frequency components identified in all contour height sequences are statistically analyzed. Based on the average frequency spectrum and the statistical analysis of characteristic frequency components, a set of spectral features capable of characterizing the frequency and energy distribution of contour undulations in the region is constructed.

[0082] Based on spectral features, a signal domain feature dataset is constructed to describe the periodic or aperiodic undulations of the surface morphology of a functional assessment sub-region. The signal domain feature dataset is an integrated and structured storage of the spectral features of all contour height sequences within a functional assessment sub-region. One approach to constructing the signal domain feature dataset is to focus on the average frequency spectrum. Discrete data points of the calculated average frequency spectrum for the functional assessment sub-region are stored as pairs of frequency values ​​and average energy values. To describe periodicity, information about periodic components can be extracted from the average frequency spectrum; if the average frequency spectrum has a sharp peak at a certain frequency, and the energy of this peak exceeds a certain multiple (e.g., 10 times) of the average energy of its left and right adjacent frequency points, then the frequency corresponding to this peak is considered to correspond to a periodic surface undulation. The frequency value, peak energy value, and half-width at half-maximum (WHM) of such periodic components can be stored as a set of features in the signal domain feature dataset. To describe aperiodic or random undulations, the overall shape of the average frequency spectrum can be analyzed; for example, the rate at which the energy of the average frequency spectrum decays with frequency can be calculated, which can be obtained by calculating the slope of the linear fit of the frequency spectrum on logarithmic coordinates. Furthermore, the energy proportion within a specific frequency band can be calculated, for example, the ratio of the total energy within a frequency band with spatial frequencies ranging from 0.1 cycles per millimeter to 1 cycle per millimeter to the total energy across the entire frequency band. The signal domain feature dataset contains the following data items: a discrete array of average frequency spectrum data; a list of parameters for identified periodic components; frequency band energy statistics characterizing aperiodic fluctuations; and characteristic frequency distribution statistics. This signal domain feature dataset is organized in the form of a numerical matrix or list, comprehensively characterizing the surface topography of the sub-region being evaluated in the frequency domain.

[0083] S5. Extract the set of feature parameters from each signal domain feature dataset to quantify the surface quality status of the functional evaluation sub-region. Specifically, this is implemented as follows:

[0084] The spectral features are read from the signal domain feature dataset. In practice, for each functional evaluation sub-region, its corresponding signal domain feature dataset has been constructed and stored in computer memory or storage media in the preceding steps. Reading the spectral features involves accessing and loading specific data items from this signal domain feature dataset. The spectral features primarily contain average frequency spectrum data, stored as an array. Each element in the array is a data pair, containing a frequency value and a corresponding average energy value. The frequency values ​​are measured in cycles per millimeter, typically sorted in ascending order, covering the range from 0 to the Nyquist frequency. The average energy value is obtained by averaging the spectra of all contour height sequences within the functional evaluation sub-region at the corresponding frequency points; its dimension is the square of the original contour height values, typically expressed in millimeters squared. Additionally, the spectral features may also contain a list of identified periodic component parameters. Each item in the list records the characteristic frequency, peak energy, and full width at half maximum (FWHM) of a periodic component. The read operation is completed by calling the corresponding data interface in the program, loading the average frequency spectrum array and the list of periodic component parameters into the program's working variables, providing input for subsequent parameter calculations.

[0085] The dominant frequency band energy proportion parameter, which characterizes the energy concentration of a surface profile, is calculated based on spectral characteristics. The dominant frequency band refers to the continuous frequency range in the average frequency spectrum where energy is most concentrated. The first step in calculating the dominant frequency band energy proportion parameter is to identify its range. One specific method for identifying the dominant frequency band is based on the energy peak value of the average frequency spectrum. First, find the frequency point with the highest energy value in the average frequency spectrum; this frequency is called the dominant peak frequency. Then, using the dominant peak frequency as the center, search in both decreasing and increasing frequency directions until a position is found where the energy value decays to a certain proportion of the dominant peak energy value, for example, 10%. The frequencies corresponding to these two positions are defined as the lower and upper limits of the dominant frequency band, respectively, and the interval between them is the dominant frequency band. Another identification method is based on cumulative energy distribution; calculate the cumulative energy curve of the average frequency spectrum starting from 0 frequency. When the cumulative energy reaches a certain proportion of the total energy, such as 80%, the corresponding frequency range is defined as the dominant frequency band. If the average frequency spectrum energy distribution is relatively flat and lacks a distinct main peak, the main frequency band can be defined as a pre-defined specific frequency band, such as the spatial frequency range between 0 cycles per millimeter and 0.5 cycles per millimeter. After determining the lower and upper limits of the main frequency band, the energy proportion parameter of the main frequency band is calculated. The formula for this parameter is: the energy proportion parameter of the main frequency band equals the sum of the average energy values ​​corresponding to all frequency points within the main frequency band, divided by the sum of the average energy values ​​corresponding to all frequency points within the entire frequency band. This calculation is implemented through numerical integration or discrete summation programming. The energy proportion parameter of the main frequency band is a dimensionless value between 0 and 1; the larger its value, the more concentrated the energy of the surface profile undulations is within a specific frequency band.

[0086] The harmonic component energy ratio parameter, which characterizes the significance of periodic undulations in a surface profile, is calculated based on spectral characteristics. Periodic undulations typically mean that there is not only a fundamental frequency peak in the spectrum, but also peaks at integer multiples of that frequency. The harmonic component energy ratio parameter aims to quantify the relative intensity of these harmonic components. The first step in calculating this parameter is to identify potential fundamental frequencies and their harmonics from a list of periodic component parameters contained in the spectral characteristics. One identification method is to first select the component with the lowest characteristic frequency and significant energy as a candidate fundamental frequency from the list of periodic component parameters. Then, check if there are other components in the list whose frequencies are approximately integer multiples of the candidate fundamental frequency. Determining whether they are approximately equal can be achieved by setting a relative tolerance, which can be set according to the resolution of the frequency spectrum or practical application experience, for example, a tolerance of 5% of the fundamental frequency. If at least one harmonic component is found, the harmonic component energy ratio parameter is calculated. The harmonic component energy ratio parameter is equal to the sum of the peak energy values ​​of all harmonic components divided by the sum of the peak energy value of the fundamental frequency component and the sum of the peak energy values ​​of all harmonic components. In specific calculations, the peak energy value of the fundamental frequency component and the peak energy values ​​of each harmonic component are extracted from the list of periodic component parameters, and the calculation is achieved through programming and accumulation. The harmonic component energy ratio parameter is also a dimensionless value between 0 and 1. The closer its value is to 1, the higher the proportion of harmonic energy to the total periodic energy. If no obvious harmonic components can be identified from the spectral characteristics, the harmonic component energy ratio parameter can be assigned a value of 0, indicating that the periodic undulating harmonic characteristics are not significant.

[0087] The dominant frequency band energy ratio parameter and the harmonic component energy ratio parameter are combined into a feature parameter set. For the currently processed functional evaluation sub-region, after the aforementioned calculations, two specific numerical parameters are obtained: the dominant frequency band energy ratio parameter and the harmonic component energy ratio parameter. Constructing the feature parameter set involves organizing these two parameters into an ordered data set according to a predetermined order. The combination method is to create an array containing two elements, for example, using the dominant frequency band energy ratio parameter as the first element and the harmonic component energy ratio parameter as the second element. In a computer program, this can be represented as a floating-point array of length 2. This feature parameter set represents the core indicators extracted from the signal domain feature dataset for quantitatively evaluating the surface quality state of the functional evaluation sub-region. The feature parameter set generated for each functional evaluation sub-region is stored in memory and associated with the identifier of that functional evaluation sub-region for use in subsequent quality comparison and judgment steps.

[0088] S6. Compare the feature parameter set with the preset benchmark parameter range for each functional evaluation sub-region to determine whether the quality of the drill pipe thickening transition zone is qualified. The specific implementation is as follows:

[0089] For each functional evaluation sub-region, each parameter in the feature parameter set of the functional evaluation sub-region is compared one by one with the corresponding parameter range in the preset benchmark parameter range for the corresponding functional evaluation sub-region, generating the parameter compliance status of the corresponding functional evaluation sub-region. In specific implementation, a benchmark parameter range is preset for each functional evaluation sub-region type obtained by step S3. The establishment of the benchmark parameter range is based on statistical analysis of a large number of known qualified cone surface samples in the thickened transition zone of the drill pipe. The statistical process includes: collecting these qualified samples, performing steps S1 to S5 of the method of this invention on each sample, calculating the feature parameter set of each sample in each functional evaluation sub-region; for the same type of functional evaluation sub-region, collecting the values ​​of the same feature parameter calculated by all qualified samples in that region; calculating the statistical mean and standard deviation of these values; finally, setting the lower limit of the benchmark parameter range of the feature parameter to the statistical mean minus 2 times the standard deviation, and setting the upper limit to the statistical mean plus 2 times the standard deviation. The benchmark parameter range is stored in the database in the form of a data table, which records the functional evaluation sub-region type, feature parameter name, lower limit of parameter compliance, and upper limit of parameter compliance. During the comparison process, for each functional evaluation sub-region of the drill pipe being tested, the program reads the feature parameter set of that sub-region and simultaneously queries the database for the corresponding baseline parameter range based on the type identifier of that sub-region. The comparison process is as follows: It determines whether the dominant frequency band energy ratio parameter in the feature parameter set is within the closed interval formed by the corresponding lower and upper acceptable limits; simultaneously, it determines whether the harmonic component energy ratio parameter is within the closed interval formed by its corresponding lower and upper acceptable limits. If a parameter value falls within its acceptable interval, the comparison status is recorded as compliant; if a parameter value falls outside its acceptable interval, the comparison status is recorded as non-compliant. After all parameters for a functional evaluation sub-region are compared, the parameter compliance status for that sub-region is generated. The parameter compliance status can be represented as a state vector consisting of multiple Boolean values, each Boolean value corresponding to the comparison result of a parameter.

[0090] Based on the parameter compliance status of all functional assessment sub-regions and combined with the preset quality influence weights of each functional assessment sub-region, the overall quality status is determined to meet the qualification conditions, thereby determining whether the quality of the drill pipe thickening transition zone is qualified. The preset quality influence weights of each functional assessment sub-region reflect the relative importance of different regions to the overall reliability of the drill pipe thickening transition zone. The setting of quality influence weights is mainly based on engineering failure analysis statistics and finite element stress simulation results. For example, by analyzing historical failure cases, regions with a high probability of crack initiation are identified; the quality influence weights of the functional assessment sub-regions corresponding to these regions are set to higher values, such as 0.3 or 0.4. Alternatively, by calculating the stress distribution of the drill pipe under typical loads through finite element analysis, higher quality influence weights are assigned to the functional assessment sub-regions corresponding to high-stress regions where the equivalent stress value exceeds 80% of the material's yield strength. The sum of the quality influence weights of all functional assessment sub-regions is set to 1. A specific method for determining whether the overall quality status meets the qualification conditions is the weighted scoring method. The weighted scoring method includes the following steps: First, calculate the sub-region score for each functional assessment sub-region. The calculation rule is that if all parameters in the parameter compliance status of a functional assessment sub-region are compliant, the sub-region score is 1; if one or more parameters are non-compliant, the sub-region score is 0. Next, calculate the overall quality score. The overall quality score equals the sub-region score of each functional assessment sub-region multiplied by the preset quality influence weight for that sub-region, and then the products of all functional assessment sub-regions are summed. Finally, determine whether the overall quality status meets the qualification criteria. The qualification criteria are defined by setting an overall qualification threshold. The setting of the overall qualification threshold comprehensively considers quality control requirements and the acceptable level of engineering risk; for example, the overall qualification threshold can be set to 0.85. The judgment logic is as follows: if the calculated overall quality score is greater than or equal to the overall pass threshold, the overall quality status is deemed to meet the pass conditions, and the quality of the thickened transition zone of the drill pipe is thus deemed passable; if the overall quality score is less than the overall pass threshold, the overall quality status is deemed not to meet the pass conditions, and the quality of the thickened transition zone of the drill pipe is thus deemed failable. The judgment result of whether the quality of the thickened transition zone of the drill pipe is passable is output with a clear identifier, completing the entire inspection process.

[0091] The overall pass threshold is set at 0.85, a value that balances the stringency of quality control with the tolerance for engineering risks. By analyzing the score distribution of historical pass samples and considering the existence of rejection rules for key sub-regions, setting the threshold at this level ensures that the vast majority of pass products pass while effectively intercepting products with significant local defects, thus achieving a practically accepted optimal trade-off between reliability and economy.

[0092] Example 2: Figure 2 A schematic diagram of the drill pipe thickening transition zone quality inspection system based on internal conical surface scanning of the present invention is provided. The drill pipe thickening transition zone quality inspection system based on internal conical surface scanning includes the following modules:

[0093] The point cloud acquisition module is used to insert the detection part of the scanning device into the inner hole of the drill pipe, drive the scanning device to move along the drill pipe axis, and simultaneously scan the inner conical surface of the thickened transition zone of the drill pipe to acquire the point cloud data of the inner conical surface.

[0094] The 3D modeling module is used to process point cloud data and obtain a 3D dataset of the inner cone surface that represents the complete morphology of the inner cone surface.

[0095] The partitioning module is used to calculate the micro-geometric features of each local region of the inner cone surface based on the three-dimensional dataset of the inner cone surface, and to divide the three-dimensional dataset of the inner cone surface into multiple functional evaluation sub-regions according to the distribution of the micro-geometric features.

[0096] The signal transformation module is used to perform signal transformation analysis on the local three-dimensional topography data corresponding to the functional evaluation sub-region in the three-dimensional dataset of the inner cone surface, and to obtain the signal domain feature dataset corresponding to the functional evaluation sub-region.

[0097] The parameter extraction module is used to extract the set of feature parameters from each signal domain feature dataset to quantify the surface quality status of the sub-region of the functional evaluation function.

[0098] The quality assessment module is used to compare the set of feature parameters with the preset benchmark parameter range for each functional evaluation sub-region to determine whether the quality of the thickened transition zone of the drill pipe is qualified.

[0099] All calculations involved in the embodiments are dimensionless numerical calculations, and the preset parameters and thresholds in the calculations are set by those skilled in the art according to the actual situation.

[0100] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0101] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and inventive constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0102] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0103] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0104] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0105] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for quality inspection of the thickened transition zone of drill pipe based on inner conical surface scanning, characterized in that, Includes the following steps: S1. Insert the detection part of the scanning device into the inner hole of the drill rod, drive the scanning device to move along the axial direction of the drill rod, and simultaneously scan the inner conical surface of the thickened transition zone of the drill rod to obtain the point cloud data of the inner conical surface; S2. Process the point cloud data to obtain a three-dimensional dataset of the inner cone surface that represents the complete morphology of the inner cone surface; S3. Calculate the microscopic geometric features of each local region of the inner cone surface based on the three-dimensional dataset of the inner cone surface, and divide the three-dimensional dataset of the inner cone surface into multiple functional evaluation sub-regions according to the distribution of the microscopic geometric features, including: Define multiple local analysis windows on the inner cone surface 3D dataset; Within each local analysis window, the complexity feature quantity representing the surface irregularity is calculated based on the spatial distribution of the three-dimensional coordinate points within the window, and the directional feature quantity representing the consistency of the surface texture direction is also calculated. The complexity and directional features of each local analysis window are combined to form a feature vector that characterizes the micro-geomorphology of each local region. Based on the aggregation relationship of the feature vectors of all local analysis windows in the feature space, the inner cone surface 3D dataset is divided into multiple functional evaluation sub-regions; S4. Perform signal transformation analysis on the local three-dimensional topography data corresponding to the functional evaluation sub-region in the three-dimensional dataset of the inner cone surface to obtain the signal domain feature dataset corresponding to the functional evaluation sub-region, including: For each functional evaluation sub-region, extract the contour height sequence along the generatrix of the inner cone surface from the corresponding local 3D topography data; Spectral analysis of the contour height sequence yields spectral characteristics that characterize the frequency and energy distribution of contour undulations. Based on spectral characteristics, a signal domain feature dataset is constructed to describe the periodic or non-periodic fluctuations in the surface morphology of the functional evaluation sub-region. S5. Extract the feature parameter set from each signal domain feature dataset to quantify the surface quality state of the functional evaluation sub-region, including: Read spectral features from the signal domain feature dataset; The parameters representing the energy concentration of the surface profile are calculated based on the spectral characteristics; the parameters representing the harmonic component energy ratios are calculated based on the spectral characteristics. The main frequency band energy ratio parameter and the harmonic component energy ratio parameter are combined into a characteristic parameter set; S6. Compare the feature parameter set with the preset benchmark parameter range for each functional evaluation sub-region to determine whether the quality of the drill pipe thickening transition zone is qualified.

2. The method for quality inspection of the thickened transition zone of drill pipe based on inner conical surface scanning according to claim 1, characterized in that, The scanning device's probe is inserted into the drill pipe's inner bore, and the scanning device is driven to move along the drill pipe's axial direction, simultaneously scanning the inner conical surface of the thickened transition zone of the drill pipe to acquire point cloud data of the inner conical surface, including: The drive scanning device moves its detection part along the axial direction of the drill pipe's inner hole; During the movement, the control and detection section scans the inner cone surface along its generatrix direction; The spatial coordinates of the inner cone surface points collected by the detection part at each axial position and circumferential angle are recorded simultaneously to form the point cloud data of the inner cone surface.

3. The method for quality inspection of the thickened transition zone of drill pipe based on inner conical surface scanning according to claim 1, characterized in that, Processing point cloud data yields a 3D dataset of the inner cone surface, representing its complete morphology, including: Based on the relative positional relationship of spatial coordinates in point cloud data with an inner cone surface, coordinate transformation and alignment are performed on the point cloud data. Based on the aligned point cloud data, a continuous inner cone surface model is constructed. Based on the inner cone surface model, a three-dimensional dataset containing the three-dimensional coordinates and topological connections of each point on the inner cone surface is generated.

4. The method for quality inspection of the thickened transition zone of drill pipe based on inner conical surface scanning according to claim 1, characterized in that, Based on the aggregation relationship of the feature vectors of all local analysis windows in the feature space, the inner cone 3D dataset is divided into multiple functional evaluation sub-regions, including: By analyzing the similarity measure between feature vectors, a set of data points with high cohesion in the feature space is identified; Map each set of highly cohesive data points back to the corresponding spatial region in the inner cone 3D dataset; Each mapped spatial region is defined as a functional evaluation sub-region.

5. The method for quality inspection of the thickened transition zone of drill pipe based on internal conical surface scanning according to claim 1, characterized in that, Spectral analysis of the contour height sequence yields spectral characteristics representing the frequency and energy distribution of contour undulations, including: The contour height sequence is converted into a frequency domain representation to obtain the frequency spectrum; Identify characteristic frequency components in the frequency spectrum whose energy exceeds a preset threshold; Based on the characteristic frequency components and their corresponding energies, spectral features are constructed.

6. The method for quality inspection of the thickened transition zone of drill pipe based on inner conical surface scanning according to claim 1, characterized in that, The feature parameter set is compared with the preset benchmark parameter range for each functional evaluation sub-region to determine whether the quality of the drill pipe thickening transition zone is qualified, including: For each functional evaluation sub-region, each parameter in the feature parameter set of the functional evaluation sub-region is compared one by one with the corresponding parameter range in the preset benchmark parameter range for the corresponding functional evaluation sub-region, and the parameter compliance status of the corresponding functional evaluation sub-region is generated. Based on the parameter compliance status of all functional evaluation sub-regions and combined with the preset quality influence weights of each functional evaluation sub-region, it is determined whether the overall quality status meets the qualification conditions, thereby determining whether the quality of the drill pipe thickening transition zone is qualified.

7. A drill pipe thickening transition zone quality inspection system based on inner conical surface scanning, used to implement the drill pipe thickening transition zone quality inspection method based on inner conical surface scanning as described in any one of claims 1-6, characterized in that, Includes the following modules: The point cloud acquisition module is used to insert the detection part of the scanning device into the inner hole of the drill pipe, drive the scanning device to move along the drill pipe axis, and simultaneously scan the inner conical surface of the thickened transition zone of the drill pipe to acquire the point cloud data of the inner conical surface. The 3D modeling module is used to process point cloud data and obtain a 3D dataset of the inner cone surface that represents the complete morphology of the inner cone surface. The partitioning module is used to calculate the micro-geometric features of each local region of the inner cone surface based on the three-dimensional dataset of the inner cone surface, and to divide the three-dimensional dataset of the inner cone surface into multiple functional evaluation sub-regions according to the distribution of the micro-geometric features. The signal transformation module is used to perform signal transformation analysis on the local three-dimensional topography data corresponding to the functional evaluation sub-region in the three-dimensional dataset of the inner cone surface, and to obtain the signal domain feature dataset corresponding to the functional evaluation sub-region. The parameter extraction module is used to extract the set of feature parameters from each signal domain feature dataset to quantify the surface quality status of the sub-region of the functional evaluation function. The quality assessment module is used to compare the set of feature parameters with the preset benchmark parameter range for each functional evaluation sub-region to determine whether the quality of the thickened transition zone of the drill pipe is qualified.

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