A drill bit wear detection method and apparatus

Abstract

The application provides a drill bit wear detection method and device, wherein the method comprises: scanning a target detection drill bit to obtain point cloud data of the target detection drill bit; performing three-dimensional point cloud reconstruction on the point cloud data to obtain a reconstructed model; segmenting and identifying the reconstructed model to determine a wear area of the target detection drill bit; aligning the three-dimensional reconstructed model of the wear drill bit to a model coordinate system of a new drill bit of the same type to perform registration, wherein the new drill bit is a drill bit of the same type as the target detection drill bit; and performing difference calculation based on the registration result to quantitatively determine the wear volume of the wear area. The above scheme solves the technical problems of low accuracy and efficiency in existing drill bit wear detection, and achieves the technical effect of improving the accuracy and efficiency of drill bit wear detection.

Description

Technical Field

[0001] This application belongs to the field of oil and gas exploration technology, and in particular relates to a method and device for detecting drill bit wear. Background Technology

[0002] As the most critical rock-breaking tool in oil and gas drilling, the performance of PDC drill bits directly affects drilling efficiency, wellbore quality, and construction cycle. In deep, high-temperature, and highly abrasive formations, PDC drill bits are subjected to complex axial compressive stress, impact loads, and frictional wear. Their wear patterns are diverse and unpredictable, with common wear patterns including: rake face wear, flank face wear, PDC composite chipping, fragmentation, spalling, and localized defects in the matrix. With the rapid development of unconventional reservoirs, the accuracy and efficiency of drill bit wear detection are becoming increasingly critical to the safety and economy of drilling projects.

[0003] Currently, manual observation and two-dimensional image measurement are commonly used in industrial settings to assess drill bit wear. However, manual experience is highly dependent on human expertise, making it difficult to accurately determine the degree of wear when multiple wear types are superimposed. Two-dimensional images only reflect projection information and lack depth dimension, making it difficult to describe the complex three-dimensional geometry of the drill bit. Low-precision three-dimensional scanning equipment is inadequate in noise control and detail capture, and cannot quantitatively calculate the wear volume, leading to inaccurate rework strategies and large deviations in drill bit life assessment.

[0004] There is currently no effective solution to the technical problems of low accuracy and efficiency in existing drill bit wear detection methods. Summary of the Invention

[0005] The purpose of this application is to provide a method and apparatus for detecting drill bit wear, which can improve the accuracy and efficiency of drill bit wear detection.

[0006] This application provides a method and apparatus for detecting drill bit wear, which is implemented as follows: A method for detecting drill bit wear includes: The target detection drill bit is scanned to obtain point cloud data of the target detection drill bit; A reconstructed model is obtained by performing three-dimensional point cloud reconstruction on the point cloud data; The reconstructed model is segmented and identified to determine the wear area of ​​the target detection drill bit; The reconstructed model is registered to the coordinate system of the unused drill bit, wherein the unused drill bit is the same model as the target detection drill bit; Based on the registration results, differential calculations are performed to quantitatively determine the wear volume of the wear area.

[0007] In one implementation, scanning the target detection drill bit to obtain point cloud data of the target detection drill bit includes: The target detection drill bit is scanned using a laser scanner to obtain point cloud data: ; in, For point cloud datasets, Let i be the i-th discrete point in space. for The three-dimensional coordinates are given by N, where N is the total number of discrete points.

[0008] In one implementation, a reconstructed model is obtained by performing three-dimensional point cloud reconstruction on the point cloud data, including: For each discrete point in the point cloud data, find the nearest neighbors. The average distance is calculated using the following formula: (Number of neighboring points are used as the neighborhood point set). ; in, For discrete points The corresponding average distance, For neighboring points, The number of neighboring points; Determine the mean and standard deviation of the average distance of the current discrete point. If the average distance of the current discrete point is greater than the sum of the mean and twice the standard deviation, the current discrete point is determined to be a noise point and deleted. For the neighborhood set of the current discrete point, the covariance matrix is ​​calculated according to the following formula: ; in, For the current discrete point The covariance matrix, For the discrete point The set of points in the neighborhood of the center, For the neighborhood point set The j Each area point, Represents the neighborhood point set The mean point, k represents the neighborhood point set. The number of middle neighbor points; Determine the normal vector of the current discrete point based on the covariance matrix; Based on the coordinates and normals of each discrete point, a 3D point cloud reconstruction model is obtained by using a reconstruction algorithm.

[0009] In one implementation, segmenting and identifying the reconstructed model to determine the wear area of ​​the target detection drill bit includes: The reconstructed model is segmented and identified, and the target detection drill bit is divided into multiple structural components, wherein the structural components include at least one of the following: central shaft, cutter blade, PDC composite sheet, and matrix; For the current discrete point Define the local average displacement vector: ; in, This is the local average displacement vector. For the current discrete point, Representing discrete points The corresponding neighborhood point set, For neighboring points; Projecting the local average displacement vector of the current discrete point onto the direction yields the projection result: ; in, For the projection result, The projection direction; If the projection result is negative and the absolute value is greater than the preset threshold, the area corresponding to the current discrete point is determined to be the wear area.

[0010] In one implementation, aligning the reconstructed model to the model coordinate system without the drill bit for registration includes: Align the reconstructed model to the coordinate system of the model without the drill bit, maintaining the same reference system; For each discrete point in the model without using a drill bit, find the nearest point in the reconstructed model to form a point pair; Based on the point pairs, solve the following equation to obtain the adjustment parameters that minimize the error. Update the discrete point positions of the adjustment parameters until convergence: ; in, For error, To adjust the parameters, Represents the nearest point in the reconstruction model. For discrete points in the model where no drill bit was used, Let j be the center point of the PDC tooth in the model without a drill bit. Let K be the center point of the j-th PDC tooth in the reconstructed model, and K represent the number of PDC tooth center points. The constraint term weighting coefficient represents the center point of the PDC tooth. This represents the translation vector.

[0011] In one implementation, differential calculations are performed based on the registration results to quantitatively determine the wear volume of the wear region, including: For each PDC tooth and each cutter wing, statistics are performed separately according to the following formula: ; ; in, Let represent the wear volume on the j-th PDC tooth, and Δdi represent the minimum distance from the i-th wear point to the surface of the unused drill bit. This represents the surface area weight corresponding to the i-th wear point. Let j be the set of wear points on the j-th PDC tooth. This represents the wear volume on the k-th blade. Let k be the set of wear points on the k-th blade. Calculate the total wear volume using the following formula: ; in, This represents the total wear volume.

[0012] In one implementation, after performing differential calculations based on the registration results to quantitatively determine the wear volume of the wear region, the method further includes: Obtain the design parameters and evaluation criteria for the drill bit type corresponding to the target detection drill bit; Based on the design parameters and evaluation criteria, determine the maximum allowable wear volume threshold; The degree of wear is determined based on the wear volume of the wear area and the maximum allowable wear volume threshold. Based on the degree of wear, determine whether the target detection drill bit needs to be replaced.

[0013] A drill bit wear detection device, comprising: The scanning module is used to scan the target detection drill bit to obtain the point cloud data of the target detection drill bit; The reconstruction module is used to obtain a reconstruction model by performing three-dimensional point cloud reconstruction on the point cloud data; The identification module is used to segment and identify the reconstructed model to determine the wear area of ​​the target detection drill bit; The registration module is used to align the reconstructed model to the model coordinate system of the unused drill bit for registration, wherein the unused drill bit is a drill bit of the same model as the target detection drill bit; The determination module is used to perform differential calculations based on the registration results to quantitatively determine the wear volume of the wear area.

[0014] An electronic device includes a processor and a memory for storing processor-executable instructions, wherein the processor, when executing the instructions, implements the steps of the method described above.

[0015] A computer-readable storage medium having a computer program / instructions stored thereon, which, when executed by a processor, implement the steps of the above-described method.

[0016] The drill bit wear detection method and apparatus provided in this application scan a target drill bit to obtain point cloud data of the target drill bit; a reconstructed model is obtained by performing three-dimensional point cloud reconstruction on the point cloud data; the reconstructed model is segmented and identified to determine the wear area of ​​the target drill bit; the reconstructed model is aligned to the coordinate system of an unused drill bit for registration, wherein the unused drill bit is a drill bit of the same model as the target drill bit; based on the registration result, differential calculation is performed to quantitatively determine the wear volume of the wear area. In the example above, a continuous high-fidelity 3D model is obtained through scanning and reconstruction. The structure is directly divided on the reconstructed 3D model without switching to a 2D image or an independent sub-model, thus achieving a unified processing flow. The feature system is entirely based on 3D reconstruction, avoiding the accumulation of errors caused by the conversion of different modules, and making the wear identification results highly consistent. Furthermore, the geometric difference between the wear area and the original surface is directly calculated on the unified 3D model to obtain the specific wear volume. This process does not require the construction of additional mathematical models or manual measurement. All calculations are completed on the real geometry of the reconstructed model, ensuring the objectivity and high accuracy of the volume calculation. Through the deep coupling between wear identification and wear volume quantification, the accuracy and efficiency of drill bit wear detection are effectively improved. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of one embodiment of the drill bit wear detection method provided in this application; Figure 2 This is a flowchart of the method for obtaining a reconstructed model from a 3D point cloud provided in this application; Figure 3 This is a schematic diagram of the model structure of one embodiment provided in this application; Figure 3 This is a hardware structure block diagram of an electronic device for a drill bit wear detection method provided in this application; Figure 4 This is a schematic diagram of the module structure of one embodiment of the drill bit wear detection device provided in this application. Detailed Implementation

[0019] To enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this application.

[0020] It should be noted that the information and data related to users involved in the embodiments of this specification are all information and data authorized by the user or fully authorized by the relevant parties. Furthermore, the collection, storage, use, processing, transmission, provision, disclosure, and application of the relevant data all comply with relevant laws, regulations, and standards, and necessary confidentiality measures have been taken. They do not violate public order and good morals, and corresponding operation entry points are provided for users or relevant parties to choose to authorize or refuse.

[0021] It should also be noted that in the embodiments of this specification, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.

[0022] Figure 1 This is a flowchart of one embodiment of the drill bit wear detection method provided in this application. Although this application provides method operation steps or apparatus structures as shown in the following embodiments or figures, more or fewer operation steps or module units may be included in the method or apparatus based on conventional or non-inventive effort. In steps or structures where there is no logically necessary causal relationship, the execution order of these steps or the module structure of the apparatus is not limited to the execution order or module structure described in the embodiments and figures of this application. When the method or module structure is applied in actual devices or end products, it can be executed sequentially or in parallel according to the method or module structure shown in the embodiments or figures (e.g., in a parallel processor or multi-threaded processing environment, or even a distributed processing environment).

[0023] Specifically, such as Figure 1 As shown, the above-mentioned drill bit wear detection method may include the following steps: Step 101: Scan the target detection drill bit to obtain the point cloud data of the target detection drill bit; Specifically, used drill bits (i.e., target detection drill bits) can be laser-scanned, while new drill bits of the same model can be retrieved from the warehouse and scanned. The drill bits are then placed on a turntable for a 360° omnidirectional scan to obtain point cloud data. ; in, For point cloud datasets, Let i be the i-th discrete point in space. for The three-dimensional coordinates are given by N, where N is the total number of discrete points.

[0024] Step 102: Obtain a reconstructed model by performing 3D point cloud reconstruction on the point cloud data; During 3D reconstruction, it can be done according to the following: Figure 2 Execute in the following manner: S201: For each discrete point in the point cloud data, find multiple neighboring points as a neighborhood point set, and calculate the average distance according to the following formula: ; in, For discrete points The corresponding average distance, For neighboring points, The number of neighboring points; S202: Determine the mean and standard deviation of the average distances to the current discrete points; S203: If the average distance of the current discrete point is greater than the sum of the average value and twice the standard deviation, the current discrete point is determined to be a noise point and deleted. S204: For the neighborhood set of the current discrete point, calculate the covariance matrix according to the following formula: ; in, For the current discrete point The covariance matrix, For the discrete point The set of points in the neighborhood of the center, For the neighborhood point set The j Each area point, Represents the neighborhood point set The mean point, k represents the neighborhood point set. The number of middle neighbor points; S205: Determine the normal vector of the current discrete point based on the covariance matrix; S206: Based on the point coordinates and normals of each discrete point, a 3D point cloud reconstruction model is obtained by using a reconstruction algorithm.

[0025] The point coordinates are the point cloud data output by the scanning device. The point cloud dataset is obtained by scanning with a laser scanner. Each discrete point records three-dimensional coordinates. The normal is calculated from the covariance matrix PCA. Principal component analysis is performed using the neighborhood covariance matrix. The eigenvector corresponding to the smallest eigenvalue is the normal of that point.

[0026] Step 103: Segment and identify the reconstructed model to determine the wear area of ​​the target detection drill bit; In one implementation, segmenting and identifying the reconstructed model to determine the wear area of ​​the target detection drill bit may include: S1: The reconstructed model is segmented and identified, and the target detection drill bit is divided into multiple structural components, wherein the structural components include at least one of the following: central shaft, cutter blade, PDC composite plate, and matrix; S2: For the current discrete point Define the local average displacement vector: ; in, This is the local average displacement vector. For the current discrete point, Representing discrete points The corresponding neighborhood point set, For neighboring points; S3: Project the local average displacement vector of the current discrete point onto the direction to obtain the projection result: ; in, For the projection result, The projection direction; S4: If the projection result is negative and the absolute value is greater than the preset threshold, determine the area corresponding to the current discrete point as the wear area.

[0027] In another implementation, wear area identification can also be performed using model recognition. Specifically, a deep learning model is trained based on three-dimensional feature parameters (e.g., curvature, rate of change of normal, local indentation, etc.). The reconstructed three-dimensional model is then subjected to "geometric partitioning" and "feature extraction." Based on the drill bit structure, the model is divided into: central axis, cutter wings, PDC composite plate, matrix, etc. Then, for each mesh vertex or point cloud point in the three-dimensional model, the following geometric quantities are calculated: principal curvature used to measure the degree of surface bending. and The system includes a curvature gradient for identifying sharp edges and a normal change rate for identifying the wear (rake face and flank face wear) of the gentle slope region. The geometric quantities are then input into the deep learning model to determine the wear region and wear type. The wear type can be divided into: no wear features, normal wear, cutting tooth / edge fracture, cutting tooth / edge breakage, and tooth / edge loss, etc., thus enabling automatic identification of multiple wear types.

[0028] Step 104: Align the reconstructed model to the coordinate system of the unused drill bit for registration, wherein the unused drill bit is a drill bit of the same model as the target detection drill bit; Specifically, aligning the reconstructed model to the coordinate system of the model without a drill bit for registration may include: S1: Align the reconstructed model to the coordinate system of the model without the drill bit, maintaining the same reference system; S2: For each discrete point in the model without using a drill bit, find the nearest point in the reconstructed model to form a point pair; S3: Based on the point pairs, solve the following equation to obtain the adjustment parameters that minimize the error. Update the discrete point positions of the adjustment parameters until convergence: ; in, For error, To adjust the parameters, Represents the nearest point in the reconstruction model. For discrete points in the model where no drill bit was used, Let j be the center point of the PDC tooth in the model without a drill bit. Let K be the center point of the j-th PDC tooth in the reconstructed model, and K represent the number of PDC tooth center points. The constraint term weighting coefficient represents the center point of the PDC tooth. This represents the translation vector.

[0029] Step 105: Based on the registration results, perform differential calculations to quantitatively determine the wear volume of the wear area.

[0030] In actual implementation, differential calculation can be performed by separately calculating each PDC tooth and each cutter wing according to the following formula: ; ; in, Let represent the wear volume on the j-th PDC tooth, and Δdi represent the minimum distance from the i-th wear point to the surface of the unused drill bit. This represents the surface area weight corresponding to the i-th wear point. Let j be the set of wear points on the j-th PDC tooth. This represents the wear volume on the k-th blade. Let k be the set of wear points on the k-th blade. Calculate the total wear volume using the following formula: ; in, This represents the total wear volume.

[0031] To effectively apply the wear identification results, after performing differential calculations based on the registration results to quantitatively determine the wear volume of the wear area, the design parameters and evaluation criteria of the drill bit type corresponding to the target detection drill bit can also be obtained; based on the design parameters and evaluation criteria, the maximum allowable wear volume threshold is determined; based on the wear volume of the wear area and the maximum allowable wear volume threshold, the wear degree is determined; based on the wear degree, it is determined whether the target detection drill bit needs to be replaced.

[0032] For example, based on the design parameters of different PDC drill bits and the IADC evaluation standard, the maximum allowable wear volume threshold can be set, and correspondingly, the current remaining lifespan of the drill bit can be estimated as follows: ; Based on this, it is possible to If the value is ≤0.3, it is determined to be slight wear; When the wear is between 0.3 and 0.7, it is determined to be moderate wear; If the wear is ≥0.7, it is determined to be severe wear, and replacement or repair is recommended.

[0033] In the example above, a high-precision laser scan was used to acquire point clouds of the entire drill bit surface. Statistical filtering, normal estimation, and reconstruction were then employed to generate a continuous, high-fidelity 3D model. This model fully preserves detailed features such as the chamfer of the composite blade, the edge of the cutting edge, and defects in the drill bit, providing a unified geometric basis for intelligent wear identification. 3D reconstruction is the core foundation for all subsequent analyses (feature extraction, classification, and volume calculation), achieving data source unification. Structural partitioning is performed directly on the reconstructed 3D model without switching to 2D images or independent sub-models, enabling a unified processing flow. Based on this, extracted parameters such as principal curvature, curvature gradient, rate of change of normal, and local concavity constitute a unified 3D feature system, ensuring consistent input data sources and strong geometric constraints for wear identification. This feature system is entirely based on 3D reconstruction, avoiding error accumulation caused by conversion between different modules and ensuring highly consistent wear identification results. Unlike existing classification methods that rely solely on 2D images or local point clouds, this method performs integrated depth identification of wear types based on a complete 3D drill bit morphology model. By inputting 3D point clouds and their geometric features into deep learning networks such as PointNet++, the model can simultaneously utilize surface structure, spatial topology, and local geometric changes to automatically classify various wear modes, including normal wear, rake face wear, flank face wear, chipping, fragmentation, spalling, tooth loss, and body defects. Furthermore, after high-precision point cloud registration between the old and new drill bit models, in this example, the geometric difference between the wear area and the original surface is directly calculated on the unified 3D model to obtain the wear volume of each composite blade and each blade. This process requires no additional mathematical model construction or manual measurement; all calculations are performed based on the true geometry of the reconstructed model, ensuring the objectivity and high accuracy of the volume calculation. Through deep coupling between wear identification and wear volume quantification, a closed-loop system from morphology to identification to quantification is formed.

[0034] The above method will be described below with reference to a specific embodiment. However, it should be noted that this specific embodiment is only for better illustration of this application and does not constitute an improper limitation of this application.

[0035] Existing methods for detecting PDC drill bit wear mainly rely on manual experience or analysis of two-dimensional images, which cannot accurately assess the complex three-dimensional structure of the drill bit, especially the precise quantification of wear. At the same time, existing methods cannot effectively identify various wear types of PDC drill bits, such as rake face wear, flank face wear, composite blade chipping, spalling, thermal cracks, and local defects in the matrix, resulting in highly subjective and poor repeatability of the test results.

[0036] To address the technical limitations of existing detection methods, such as low accuracy, inability to perform quantitative analysis, and lack of intelligent recognition, this paper presents a PDC drill bit wear detection method based on laser scanning 3D reconstruction and point cloud registration. High-precision laser scanning acquires the 3D morphology of the drill bit, and point cloud reconstruction and intelligent segmentation accurately extract the wear area. An improved strong registration algorithm achieves high-precision alignment of the old and new drill bit models, ultimately obtaining a quantitative calculation of the wear volume. This enables high-precision, full 3D detection of PDC drill bit wear, automatic identification of wear types, and improved judgment accuracy. It also allows for precise calculation of wear volume, obtaining the true wear volume of the composite blades and cutter wings through model difference, improving detection efficiency and consistency. This provides a reliable basis for drill bit rework, service life management, and drill bit parameter optimization design.

[0037] Specifically, a method for detecting PDC drill bit wear based on high-precision laser scanning, 3D point cloud reconstruction, wear type identification, and strong model registration is provided. By performing a full-space scan of the drill bit, establishing a 3D topographic model, and comparing the old and new drill bit models to obtain the actual wear volume, a high-precision quantitative assessment of PDC drill bit wear is achieved. This method may include the following steps: S1: Laser scanning and point cloud acquisition: Used drill bits are laser-scanned after use, while new drill bits of the same model are retrieved from the warehouse and scanned simultaneously. The drill bits are then placed on a turntable for a 360° all-around scan.

[0038] Let the point cloud obtained by scanning be: ; Each of them Let N be a discrete point in space, recording the three-dimensional coordinates of a certain location on the drill bit surface, where N is the total number of points.

[0039] S2: Point cloud preprocessing and 3D reconstruction: For each point Find its nearest Calculate the average distance between neighboring points: ; Count all points average value and standard deviation If a certain point satisfies If the point is far from the main point cloud group, it is determined to be an outlier or noise point and is deleted. Then, for the point... neighborhood point set Calculate the covariance matrix: ; After obtaining the point coordinates and normals, a continuous three-dimensional surface of the drill bit is constructed using a reconstruction algorithm.

[0040] S3: Structural Region Division and Feature Extraction: The reconstructed model is then subjected to "geometric partitioning" and "feature extraction." Based on the drill bit structure, the model is divided into: central axis, cutter wings, PDC composite blades, and matrix, etc.

[0041] For each mesh vertex or point cloud point, calculate the following geometric quantity: principal curvature used to measure the degree of surface curvature. and Curvature gradient for identifying sharp edges; normal change rate for identifying wear (rake face and flank face wear) in gentle slope regions.

[0042] S4: Wear Region Segmentation and Wear Type Identification: Wear types can be broadly categorized as: no wear characteristics, normal wear, cutting tooth / edge fracture, cutting tooth / edge breakage, and tooth / edge loss.

[0043] For point Define its local average displacement vector: ; Will In direction Projecting upwards: ; if If the value is negative and the absolute value is large, it indicates that the adjacent point is "lower" than the ideal surface where the point is located, which is a concave shape, and therefore can be identified as a wear and tear area.

[0044] In practical implementation, a deep learning model can be trained based on three-dimensional feature parameters (e.g., curvature, rate of change of normal, local concavity, etc.). The reconstructed three-dimensional model is then subjected to "geometric partitioning" and "feature extraction." Based on the drill bit structure, the model is divided into: central axis, blade, PDC composite piece, matrix, etc. Then, for each mesh vertex or point cloud point in the three-dimensional model, the following geometric quantities are calculated: curvature, rate of change of normal, and local concavity. These geometric quantities are then input into the aforementioned deep learning model to determine the wear area and wear type. The wear type can be divided into: no wear features, normal wear, cutting tooth / edge fracture, cutting tooth / edge fragmentation, and tooth / edge loss, etc., thereby enabling automatic identification of multiple wear types.

[0045] Accordingly, the deep learning model described above can be used for detection in the following way: ; in, The results indicate the type of wear and tear. Representing discrete points The geometric quantity, where fθ represents the wear identification model expressed by parameter θ, and θ is the model parameter of the wear identification model.

[0046] During model training, the real labels annotated by on-site experts can be used as supervision signals to optimize the loss function: ; in, The loss value. The total number of samples, The total number of categories, For the first i The sample at the th c Real labels in each category For the model to the first i The sample at the th c The predicted probabilities for each category are used to measure the difference between the model's predicted distribution and the true distribution by calculating the logarithmic error between the predicted probabilities and the true labels.

[0047] S5: Strong registration of 3D models of old and new drill bits: Align the old drill bit model with the coordinate system of the new drill bit model, maintaining the same reference frame. For Find each point Find the nearest points in the array. Based on this pair of points, find the optimal R and t that minimize the error E. Update the array with the new R and t. The position until convergence: ; in, The center point of the j-th PDC tooth of the new drill bit. This is the center point of the j-th PDC tooth of the old drill bit.

[0048] S6: Quantitative calculation of wear volume: After registration, the old and new models are in the same coordinate system, and difference calculations are performed.

[0049] Statistical analysis was performed for each PDC tooth and each cutter wing separately: ; ; in: Let j be the set of wear points on the j-th PDC tooth. Let be the set of wear points on the k-th blade.

[0050] The total wear volume is: .

[0051] S7: Wear Level Assessment and Lifespan Estimation: Based on the design parameters of different PDC drill bit models and the IADC evaluation standard, a maximum allowable wear volume threshold is set. Correspondingly, the current remaining lifespan of the drill bit can be estimated as follows: ; Based on this, it is possible to If the value is ≤0.3, it is determined to be slight wear; When the wear is between 0.3 and 0.7, it is determined to be moderate wear; If the wear is ≥0.7, it is determined to be severe wear, and replacement or repair is recommended.

[0052] This example also provides a drill bit wear detection device, which may include: a scanning acquisition module for controlling a laser scanner to acquire point clouds of new and old drill bits; a point cloud preprocessing and reconstruction module for performing algorithms such as noise filtering, normal estimation, and Poisson reconstruction; a feature analysis and wear identification module for dividing the 3D model into regions and identifying wear types; a registration module for executing an improved ICP algorithm to achieve rigid body registration of the new and old models; and a volume calculation and evaluation module for performing volume difference, wear level, and life assessment, and outputting the results.

[0053] Taking the wear detection of a PDC drill bit retrieved from an oil and gas well as an example, the drill bit has a size of 165.1 mm and contains 45 PDC composite pieces distributed on six blades. The process may include the following steps.

[0054] S1: Data Acquisition and Preprocessing The drill bit was scanned from multiple angles using a high-precision laser, achieving a scanning accuracy of ±20μm, resulting in raw point cloud data of approximately 32 million points. A statistical outlier filtering method was employed, utilizing the K-neighbor average distance of each point to identify noise points, thus satisfying the required parameters. Point culling is performed to remove isolated points caused by equipment vibration or abnormal light spots. Voxel downsampling is applied to areas with excessively high density to reduce the point cloud to approximately 18 million points, ensuring both model quality and data processing efficiency. The scan coordinates are aligned so that the drill bit's central axis is consistent with the Z-axis.

[0055] S2: 3D reconstruction of PDC drill bit: For each point, the neighborhood covariance matrix is ​​calculated and the principal components are computed. The eigenvector corresponding to the smallest eigenvalue is the surface normal of that point, which is used to guide the reconstruction. The final generated 3D mesh model contains approximately 3.5 million triangular facets, clearly presenting: the complete shape of the 6 blades, the planes and chamfered areas of each PDC composite piece, the surface texture of the tire body, and details such as edge chipping.

[0056] S3: Wear Area Detection and Wear Type Identification: For each point in the reconstructed model, calculate the principal curvature, normal rate of change, and local concavity / convexity to identify wear areas: curvature abrupt change areas, depression areas, and tire body detachment areas.

[0057] The point cloud was input into the trained PointNet++ model, and the wear type label for each point was output. A total of 37 PDC teeth with significant wear were identified: 16 were of normal wear, 9 were broken cutting teeth, 8 were fragmented cutting teeth, and 4 were missing teeth. Four areas of slight wear were identified on the tire carcass surface.

[0058] S4: Strong registration of old and new drill bit models: Coarse alignment is performed using key structures (central axis + blade distribution). In this example, K=45 PDC center points are selected as anchor points, and β=0.8 is set to reduce the registration error to 0.06 mm. After alignment, the old and new models overlap precisely, ensuring the effectiveness of the difference calculation.

[0059] S5: Calculation of wear volume difference: For points on the old drill bit surface that are determined to be worn, calculate the minimum distance from them to the new drill bit surface, and combine the surface area weight i of the corresponding points to calculate the volume contribution of a single point.

[0060] S6: Wear Level and Optimized Design: Based on the design of this PDC drill bit model, the maximum wear volume is 1100 mm³. According to the life assessment formula in this example, combined with the advice of on-site experts, the wear level is between light and moderate, and it can continue to be used after maintenance. The wear amount is matched with the drilling log during drilling operations, providing a basis for subsequent design optimization.

[0061] In the example above, by introducing laser scanning and high-density point cloud reconstruction technology, the true three-dimensional structure of the PDC drill bit can be completely acquired with micron-level accuracy. This overcomes the shortcomings of existing two-dimensional image methods that cannot accurately reflect complex spatial morphology, significantly improving the accuracy and repeatability of wear detection. Based on three-dimensional feature parameters (curvature, rate of change of normal, local indentation, etc.) and deep learning models, automatic identification of various wear types is achieved, thereby reducing the subjective influence of manual judgment and making the detection results more objective, stable, and repeatable. By independently calculating the wear volume of each composite piece and each cutter blade, a refined analysis of the drill bit's local structure can be performed, providing accurate data support for evaluating drill bit wear non-uniformity, optimizing drill bit structural design, and analyzing downhole stress mechanisms, thus improving the level of drill bit lifecycle management. The intelligent identification and automatic calculation process can complete all analysis in a short time, which is superior to the manual detection mode, significantly improving detection efficiency. It is applicable to various scenarios such as drilling sites and repair workshops, and is conducive to quickly determining whether the drill bit can continue to be used or needs to be repaired. Precise wear volume distribution data can reflect the wear mechanism under different formations and drilling parameters, helping drill bit manufacturers optimize blade angles, composite blade layouts, and matrix materials. It also provides drilling engineers with quantifiable wear pattern analysis, improving the overall performance and economy of drill bits.

[0062] The methods and embodiments provided in the above-described embodiments of this application can be executed in a mobile terminal, computer terminal, or similar computing device. Taking operation on an electronic device as an example... Figure 3 This is a hardware structure block diagram of an electronic device for a drill bit wear detection method provided in this application. (See diagram for example.) Figure 3 As shown, the electronic device 10 may include one or more (only one is shown in the figure) processors 02 (processors 02 may include, but are not limited to, processing devices such as microprocessors (MCUs) or programmable logic devices (FPGAs), a memory 04 for storing data, and a transmission module 06 for communication functions. Those skilled in the art will understand that... Figure 3 The structure shown is for illustrative purposes only and does not limit the structure of the electronic device described above. For example, electronic device 10 may also include... Figure 3 The more or fewer components shown, or having the same Figure 3 The different configurations shown.

[0063] The memory 04 can be used to store software programs and modules of application software, such as the program instructions / modules corresponding to the drill bit wear detection method in this embodiment. The processor 02 executes various functional applications and data processing by running the software programs and modules stored in the memory 04, thereby realizing the drill bit wear detection method of the aforementioned application. The memory 04 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 04 may further include memory remotely located relative to the processor 02, and these remote memories can be connected to the electronic device 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0064] The transmission module 06 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of the electronic device 10. In one example, the transmission module 06 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission module 06 may be a Radio Frequency (RF) module, used for wireless communication with the Internet.

[0065] At the software level, the aforementioned drill bit wear detection device can, as Figure 4 As shown, it includes: The scanning module 401 is used to scan the target detection drill bit to obtain the point cloud data of the target detection drill bit; Reconstruction module 402 is used to obtain a reconstruction model by performing three-dimensional point cloud reconstruction on the point cloud data; The identification module 403 is used to segment and identify the reconstructed model to determine the wear area of ​​the target detection drill bit; The registration module 404 is used to align the reconstructed model to the model coordinate system of the unused drill bit for registration, wherein the unused drill bit is a drill bit of the same model as the target detection drill bit; The determination module 405 is used to perform differential calculations based on the registration results to quantitatively determine the wear volume of the wear area.

[0066] In one embodiment, the scanning module 401 can specifically scan the target detection drill bit using a laser scanner to obtain point cloud data: ; in, For point cloud datasets, Let i be the i-th discrete point in space. for The three-dimensional coordinates are given by N, where N is the total number of discrete points.

[0067] In one implementation, the reconstruction module 402 can specifically find the nearest neighbors for each discrete point in the point cloud data. The average distance is calculated using the following formula: (Number of neighboring points are used as the neighborhood point set). ; in, For discrete points The corresponding average distance, For neighboring points, The number of neighboring points; Determine the mean and standard deviation of the average distance of the current discrete point. If the average distance of the current discrete point is greater than the sum of the mean and twice the standard deviation, the current discrete point is determined to be a noise point and deleted. For the neighborhood set of the current discrete point, the covariance matrix is ​​calculated according to the following formula:

[0068] in, For the current discrete point The covariance matrix, For the discrete point The set of points in the neighborhood of the center, For the neighborhood point set The j Each area point, Represents the neighborhood point set The mean point, k represents the neighborhood point set. The number of middle neighbor points; Determine the normal vector of the current discrete point based on the covariance matrix; Based on the coordinates and normals of each discrete point, a 3D point cloud reconstruction model is obtained by using a reconstruction algorithm.

[0069] In one embodiment, the identification module 403 can specifically segment and identify the reconstructed model, dividing the target detection drill bit into multiple structural components, wherein the structural components include at least one of the following: central shaft, cutter blade, PDC composite sheet, and matrix; For the current discrete point Define the local average displacement vector: ; in, This is the local average displacement vector. For the current discrete point, Representing discrete points The corresponding neighborhood point set, For neighboring points; Projecting the local average displacement vector of the current discrete point onto the direction yields the projection result: ; in, For the projection result, The projection direction; If the projection result is negative and the absolute value is greater than the preset threshold, the area corresponding to the current discrete point is determined to be the wear area.

[0070] In one implementation, the registration module 404 specifically aligns the reconstructed model to the coordinate system of the model without the drill bit, maintaining the same reference system; for each discrete point in the model without the drill bit, it finds the nearest point in the reconstructed model to form a point pair; based on the point pair, it solves the following equation to obtain the adjustment parameter that minimizes the error, and updates the discrete point positions of the adjustment parameter until convergence: ; in, For error, To adjust the parameters, Represents the nearest point in the reconstruction model. For discrete points in the model where no drill bit was used, Let j be the center point of the PDC tooth in the model without a drill bit. Let K be the center point of the j-th PDC tooth in the reconstructed model, and K represent the number of PDC tooth center points. The constraint term weighting coefficient represents the center point of the PDC tooth. This represents the translation vector; for example, K can take the value 45. It can take the value 0.8.

[0071] In one implementation, the determining module 405 can specifically perform statistics on each PDC tooth and each cutter wing according to the following formula: ; ; in, Let represent the wear volume on the j-th PDC tooth, and Δdi represent the minimum distance from the i-th wear point to the surface of the unused drill bit. This represents the surface area weight corresponding to the i-th wear point. Let j be the set of wear points on the j-th PDC tooth. This represents the wear volume on the k-th blade. Let k be the set of wear points on the k-th blade. Calculate the total wear volume using the following formula: ; in, This represents the total wear volume.

[0072] In one implementation, after performing differential calculations based on the registration results to quantitatively determine the wear volume of the wear area, the design parameters and evaluation criteria of the drill bit type corresponding to the target detection drill bit can also be obtained; the maximum allowable wear volume threshold is determined according to the design parameters and evaluation criteria; the wear degree is determined according to the wear volume of the wear area and the maximum allowable wear volume threshold; and the wear degree is used to determine whether the target detection drill bit needs to be replaced.

[0073] This application also provides a specific implementation of an electronic device capable of implementing all steps of the drill bit wear detection method in the above embodiments. The electronic device specifically includes: a processor, a memory, a communication interface, and a bus; wherein the processor, memory, and communication interface communicate with each other via the bus; the processor is used to call a computer program in the memory, and when the processor executes the computer program, it implements all steps of the drill bit wear detection method in the above embodiments. For example, when the processor executes the computer program, it implements the following steps: Step 1: Scan the target detection drill bit to obtain the point cloud data of the target detection drill bit; Step 2: Obtain the reconstructed model by performing 3D point cloud reconstruction on the point cloud data; Step 3: Segment and identify the reconstructed model to determine the wear area of ​​the target detection drill bit; Step 4: Align the reconstructed model to the coordinate system of the unused drill bit for registration, wherein the unused drill bit is the same model as the target detection drill bit; Step 5: Based on the registration results, perform differential calculations to quantitatively determine the wear volume of the wear area.

[0074] Embodiments of this application also provide a computer-readable storage medium capable of implementing all steps of the drill bit wear detection method in the above embodiments. The computer-readable storage medium stores a computer program that, when executed by a processor, implements all steps of the drill bit wear detection method in the above embodiments. For example, when the processor executes the computer program, it implements the following steps: Step 1: Scan the target detection drill bit to obtain the point cloud data of the target detection drill bit; Step 2: Obtain the reconstructed model by performing 3D point cloud reconstruction on the point cloud data; Step 3: Segment and identify the reconstructed model to determine the wear area of ​​the target detection drill bit; Step 4: Align the reconstructed model to the coordinate system of the unused drill bit for registration, wherein the unused drill bit is the same model as the target detection drill bit; Step 5: Based on the registration results, perform differential calculations to quantitatively determine the wear volume of the wear area.

[0075] As described above, this embodiment of the application scans the target detection drill bit to obtain point cloud data of the target detection drill bit; a reconstructed model is obtained by performing three-dimensional point cloud reconstruction on the point cloud data; the reconstructed model is segmented and identified to determine the wear area of ​​the target detection drill bit; the reconstructed model is aligned to the coordinate system of the unused drill bit for registration, wherein the unused drill bit is a drill bit of the same model as the target detection drill bit; based on the registration result, differential calculation is performed to quantitatively determine the wear volume of the wear area. This scheme effectively improves the accuracy and efficiency of drill bit wear detection.

[0076] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on its differences from other embodiments. In particular, hardware + program embodiments are relatively simple in description because they are fundamentally similar to method embodiments; relevant parts can be referred to the descriptions in the method embodiments.

[0077] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0078] While this application provides the method operation steps as described in the embodiments or flowcharts, more or fewer operation steps may be included based on conventional or non-inventive labor. The order of steps listed in the embodiments is merely one possible execution order among many and does not represent the only execution order. In actual device or client product execution, the methods shown in the embodiments or drawings can be executed sequentially or in parallel (e.g., in a parallel processor or multi-threaded processing environment).

[0079] While this specification provides method operation steps as described in the embodiments or flowcharts, more or fewer operation steps may be included based on conventional or non-inventive means. The order of steps listed in the embodiments is merely one possible execution order among many and does not represent the only execution order. In actual device or end product execution, the methods shown in the embodiments or drawings may be executed sequentially or in parallel (e.g., in a parallel processor or multi-threaded processing environment, or even a distributed data processing environment). The terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, product, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, product, or apparatus. Without further limitations, the presence of other identical or equivalent elements in the process, method, product, or apparatus that includes said elements is not excluded.

[0080] For ease of description, the above devices are described in terms of function, divided into various modules. Of course, in implementing the embodiments of this specification, the functions of each module can be implemented in one or more software and / or hardware components, or a module that performs the same function can be implemented by a combination of multiple sub-modules or sub-units. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division; in actual implementation, there may be other division methods. For example, multiple units 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, or indirect coupling or communication connection between devices or units, and may be electrical, mechanical, or other forms.

[0081] Those skilled in the art will also know that, besides implementing the controller in the form of purely computer-readable program code, the same functions can be achieved by logically programming the method steps, making the controller take the form of logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers (PLCs), and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the devices within it used to implement various functions can also be considered structures within that hardware component. Alternatively, the devices used to implement various functions can be considered as both software modules implementing the method and structures within a hardware component.

[0082] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0083] Those skilled in the art will understand that the embodiments of this specification can be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, the embodiments of this specification can take the form of computer program products implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0084] The embodiments described in this specification can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. The embodiments of this specification can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0085] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, system embodiments are basically similar to method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. In the description of this specification, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the embodiments in this specification. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described can be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0086] The above description is merely an embodiment of the present specification and is not intended to limit the embodiments of the present specification. For those skilled in the art, various modifications and variations can be made to the embodiments of the present specification. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the embodiments of the present specification should be included within the scope of the claims of the embodiments of the present specification.

Claims

1. A method for detecting drill bit wear, characterized in that, The method includes: The target detection drill bit is scanned to obtain point cloud data of the target detection drill bit; A reconstructed model is obtained by performing three-dimensional point cloud reconstruction on the point cloud data; The reconstructed model is segmented and identified to determine the wear area of ​​the target detection drill bit; The reconstructed model is registered to the model coordinate system without a drill bit, wherein the unused drill bit is a drill bit of the same model as the target detection drill bit; Based on the registration results, differential calculations are performed to quantitatively determine the wear volume of the wear area.

2. The method according to claim 1, characterized in that, The reconstructed model is segmented and identified to determine the wear area of ​​the target detection drill bit, including: The reconstructed model is segmented and identified, and the target detection drill bit is divided into multiple structural components, wherein the structural components include at least one of the following: central shaft, cutter blade, PDC composite sheet, and matrix; For the current discrete point Define the local average displacement vector: ； in, This is the local average displacement vector. For the current discrete point, Representing discrete points The corresponding neighborhood point set, For neighboring points; Projecting the local average displacement vector of the current discrete point onto the direction yields the projection result: ； in, For the projection result, The projection direction; If the projection result is negative and the absolute value is greater than the preset threshold, the area corresponding to the current discrete point is determined to be the wear area.

3. The method according to claim 1, characterized in that, Aligning the reconstructed model to the coordinate system of the model without a drill bit for registration includes: Align the reconstructed model to the coordinate system of the model without the drill bit, maintaining the same reference system; For each discrete point in the model without using a drill bit, find the nearest point in the reconstructed model to form a point pair; Based on the point pairs, solve the following equation to obtain the adjustment parameters that minimize the error. Update the discrete point positions of the adjustment parameters until convergence: ； in, For error, To adjust the parameters, Represents the nearest point in the reconstruction model. For discrete points in the model where no drill bit was used, Let j be the center point of the j-th PDC tooth in the model without a drill bit. Let K be the center point of the j-th PDC tooth in the reconstructed model, and K represent the number of PDC tooth center points. The constraint term weighting coefficient represents the center point of the PDC tooth. This represents the translation vector.

4. The method according to claim 1, characterized in that, The target detection drill bit is scanned to obtain point cloud data of the target detection drill bit, including: The target detection drill bit is scanned using a laser scanner to obtain point cloud data: ； in, For point cloud datasets, Let i be the i-th discrete point in space. for The three-dimensional coordinates are given by N, where N is the total number of discrete points.

5. The method according to claim 1, characterized in that, A reconstructed model is obtained by performing 3D point cloud reconstruction on the point cloud data, including: For each discrete point in the point cloud data, find the nearest neighbors. The average distance is calculated using the following formula: (Number of neighboring points are used as the neighborhood point set). ； in, For discrete points The corresponding average distance, For neighboring points, The number of neighboring points; Determine the mean and standard deviation of the average distance of the current discrete point. If the average distance of the current discrete point is greater than the sum of the mean and twice the standard deviation, the current discrete point is determined to be a noise point and deleted. For the current discrete point neighborhood point set For each neighborhood point, calculate the covariance matrix according to the following formula. : ； in, For the current discrete point The covariance matrix, For the discrete point The set of points in the neighborhood of the center, For the neighborhood point set The j Each area point, Represents the neighborhood point set The mean point, k represents the neighborhood point set. The number of middle neighbor points; Determine the normal vector of the current discrete point based on the covariance matrix; Based on the point coordinates and normals of each discrete point, a 3D point cloud reconstruction model is obtained by using a reconstruction algorithm.

6. The method according to claim 1, characterized in that, Based on the registration results, differential calculations are performed to quantitatively determine the wear volume of the wear region, including: For each PDC tooth and each cutter wing, statistics are performed separately according to the following formula: ； ； in, Let represent the wear volume on the j-th PDC tooth, and Δdi represent the minimum distance from the i-th wear point to the surface of the unused drill bit. This represents the surface area weight corresponding to the i-th wear point. Let j be the set of wear points on the j-th PDC tooth. This represents the wear volume on the k-th blade. Let k be the set of wear points on the k-th blade. Calculate the total wear volume using the following formula: ； in, This represents the total wear volume.

7. The method according to claim 1, characterized in that, After performing differential calculations based on the registration results to quantitatively determine the wear volume of the wear region, the process also includes: Obtain the design parameters and evaluation criteria for the drill bit type corresponding to the target detection drill bit; Based on the design parameters and evaluation criteria, determine the maximum allowable wear volume threshold; The degree of wear is determined based on the wear volume of the wear area and the maximum allowable wear volume threshold. Based on the degree of wear, determine whether the target detection drill bit needs to be replaced.

8. A drill bit wear detection device, characterized in that, include: The scanning module is used to scan the target detection drill bit to obtain the point cloud data of the target detection drill bit; The reconstruction module is used to obtain a reconstruction model by performing three-dimensional point cloud reconstruction on the point cloud data; The identification module is used to segment and identify the reconstructed model to determine the wear area of ​​the target detection drill bit; The registration module is used to align the reconstructed model to the model coordinate system without a drill bit for registration, wherein the unused drill bit is a drill bit of the same model as the target detection drill bit; The determination module is used to perform differential calculations based on the registration results to quantitatively determine the wear volume of the wear area.

9. An electronic device comprising a processor and a memory for storing processor-executable instructions, characterized in that, When the processor executes the instructions, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method according to any one of claims 1 to 7.

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