Hierarchical drill bit polishing state detection and evaluation method

Abstract

The invention provides a hierarchical drill bit polishing state detection and evaluation method, and relates to the technical field of data processing, and the method comprises the steps: determining preset polishing precision, carrying out hierarchical analysis, calculating a hierarchical correlation coefficient, carrying out hierarchical multivariate coordinate conversion, supervising a training precision detection module, determining a space-time detection point cloud, and combining with a signal processing model. Deviation correction and local feature enhancement processing are carried out, distributed surface features are determined, assembly analysis is carried out, a precision detection single column is determined, and terminal visual display is carried out. According to the method, the technical problem that the drilling precision and the production efficiency are further influenced due to unstable grinding quality of the surface of the drill bit caused by limited manual detection precision in the prior art can be solved, deviation correction and local feature enhancement processing are effectively performed on the space-time detection point cloud, so that the recognition degree and precision of the surface features are improved, and the production efficiency is improved. The production process is optimized, and the product quality is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and particularly relates to a hierarchical drill bit polishing state detection and evaluation method. BACKGROUND

[0002] As a commonly used tool in industrial processing, the surface precision of a drill bit directly affects the service life of the drill bit, the drilling quality and the processing efficiency. Under the background of rapid development of high-tech and precision manufacturing industry, the requirement for the polishing precision of the outer surface of the drill bit is higher and higher. Traditional detection methods mostly rely on manual experience and simple measuring tools, such as magnifying glasses, micrometers and the like. These methods are not only low in efficiency, but also limited in precision, and cannot meet the high requirement for precision in modern industry.

[0003] At present, the existing polishing precision detection technology for the outer surface of the drill bit mainly relies on manual experience and simple measuring tools, and lacks high-precision and automated detection means.

[0004] In summary, due to the limited precision of manual detection, the existing technology causes the unstable polishing quality of the surface of the drill bit, and further affects the drilling precision and production efficiency. SUMMARY

[0005] The purpose of the present application is to provide a hierarchical drill bit polishing state detection and evaluation method, so as to solve the technical problem that the existing technology causes the unstable polishing quality of the surface of the drill bit due to the limited precision of manual detection, and further affects the drilling precision and production efficiency. The present application effectively performs bias correction and local feature enhancement processing on the space-time detection point cloud, so as to improve the recognition degree and precision of the surface features, and optimizes the production process and improves the product quality.

[0006] In view of the above problems, the present application provides a hierarchical drill bit polishing state detection and evaluation method.

[0007] The application provides a hierarchical drill bit polishing state detection and evaluation method, which comprises the following steps: determining a preset polishing precision based on the machining standard of a target drill bit, performing hierarchical dissection of precision elements, and cascading a precision system, wherein the dissection levels at least include an index layer and a feature layer; traversing the cascaded precision system, calculating a hierarchical correlation coefficient, and performing hierarchical multivariate coordinate conversion to distribute baseline coordinates of the preset polishing precision and determine a hierarchical detection coordinate system; based on the hierarchical detection coordinate system, supervising and training a precision detection module; based on space-time characteristics, performing laser scanning and integration on the target drill bit to determine a space-time detection point cloud; combining a signal processing model, performing bias correction and local feature enhancement processing on the space-time detection point cloud to determine distributed surface features, wherein the feature enhancement processing mode includes joint inspection fitting and algorithm processing; based on the precision detection module, performing state evaluation and assembly analysis on the distributed surface features to determine a precision detection single column and perform terminal visual display, wherein the analysis standard includes single-point analysis and time series trend analysis.

[0008] The one or more technical solutions provided in the application have at least the following technical effects or advantages: By determining a preset polishing precision based on the machining standard of a target drill bit, performing hierarchical dissection of precision elements, and cascading a precision system, wherein the dissection levels at least include an index layer and a feature layer; traversing the cascaded precision system, calculating a hierarchical correlation coefficient, and performing hierarchical multivariate coordinate conversion to distribute baseline coordinates of the preset polishing precision and determine a hierarchical detection coordinate system; based on the hierarchical detection coordinate system, supervising and training a precision detection module; based on space-time characteristics, performing laser scanning and integration on the target drill bit to determine a space-time detection point cloud; combining a signal processing model, performing bias correction and local feature enhancement processing on the space-time detection point cloud to determine distributed surface features, wherein the feature enhancement processing mode includes joint inspection fitting and algorithm processing; based on the precision detection module, performing state evaluation and assembly analysis on the distributed surface features to determine a precision detection single column and perform terminal visual display, wherein the analysis standard includes single-point analysis and time series trend analysis, the technical problems that the drilling bit surface polishing quality is unstable due to the limited artificial detection precision, which further affects the drilling precision and production efficiency in the prior art are effectively solved, the space-time detection point cloud is effectively corrected and locally enhanced, thereby improving the recognition degree and precision of the surface features, and the production process is optimized and the product quality is improved.

[0009] The above description is only a summary of the technical solutions of the present application. In order to enable the technical means of the present application to be more clearly understood, and to be implemented according to the content of the description, and in order to enable the above and other purposes, characteristics and advantages of the present application to be more apparent and easy to understand, the following specific embodiments of the present application are described. It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only exemplary, and other drawings can be obtained by those skilled in the art without creative labor on the basis of the provided drawings.

[0011] Figure 1 A flowchart of a hierarchical drill bit polishing state detection and evaluation method of the present application; Figure 2 A flowchart of determining a hierarchical detection coordinate system in a hierarchical drill bit polishing state detection and evaluation method of the present application. DETAILED DESCRIPTION

[0012] The present application provides a hierarchical drill bit polishing state detection and evaluation method, which solves the technical problem that the surface polishing quality of the drill bit is unstable due to the limited precision of manual detection in the prior art, further affecting the drilling precision and production efficiency. The present application effectively performs bias correction and local feature enhancement processing on the space-time detection point cloud, thereby improving the recognition degree and precision of the surface features, and optimizing the production process and improving the product quality.

[0013] The technical solutions in the present application will be described clearly and completely with reference to the drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments of the present application. It should be understood that the present application is not limited by the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application. In addition, it should be noted that only parts related to the present application are shown in the drawings for convenience of description.

[0014] Embodiment one Please refer to the drawings Figure 1 The present application provides a hierarchical drill bit polishing state detection and evaluation method, which specifically comprises the following steps: S1: Based on the machining standards of the target drill bit, determine the preset polishing precision, and perform hierarchical decomposition of precision elements, cascade precision system, wherein the decomposition level at least includes index layer and feature layer.

[0015] Specifically, according to the use scene of the drill bit, such as drilling material, hole diameter size, etc., the required polishing precision is determined. For example, for occasions that require high-precision drilling, the polishing precision requirement of the drill bit will be higher. Ensure that the drill bit can achieve the expected drilling effect when in use, such as hole diameter accuracy, hole wall roughness, etc. The hierarchical decomposition of precision elements is to decompose the overall polishing precision requirement into more specific and operable precision elements, at least including index layer and feature layer. The index layer includes specific and quantitative precision indicators, such as drill bit diameter error, roundness error, surface roughness Ra value, etc. The feature layer includes key factors that affect the precision indicators, such as drill bit material, hardness, heat treatment state, machining process parameters, such as cutting speed, feed rate, cutting depth, etc. According to the mutual influence and dependence relationship between the precision elements, they are organized into a hierarchical system. In this system, high-level elements guide and constrain low-level elements.

[0016] S2: Traverse the cascade precision system, calculate the hierarchical correlation coefficient, and perform hierarchical multivariate coordinate transformation to distribute the baseline coordinates of the preset polishing precision, and determine the hierarchical detection coordinate system.

[0017] Specifically, starting from the top layer of the system, traverse each precision element layer by layer. For each pair of related precision elements, such as the elements between the index layer and the feature layer, calculate the correlation coefficient between them. The correlation coefficient can reflect the degree of association between two elements, such as Pearson correlation coefficient, Spearman rank correlation coefficient, etc. Based on the calculated correlation coefficient, each precision element is converted into a coordinate point in a multi-dimensional space. The dimension of the coordinate corresponds to the number of elements and the level of the cascade precision system. During the conversion process, principal component analysis or other dimension reduction techniques can be used to simplify the coordinate system. In the transformed multi-dimensional space, a baseline coordinate is determined according to the preset polishing precision requirement. This baseline coordinate represents the position of the drill bit that meets the preset precision requirement in the multi-dimensional space. By comparing the distance between the actual drill bit coordinate and the baseline coordinate, it can be evaluated whether the polishing precision of the drill bit meets the requirements. Take the baseline coordinate as the center to establish a hierarchical detection coordinate system. In this coordinate system, each dimension corresponds to a precision element, and the scale on the coordinate axis represents the precision level of the element.

[0018] S3: Based on the hierarchical detection coordinate system, supervise the training of the precision detection module.

[0019] Specifically, historical data of drill bit grinding accuracy is collected, including measurement results of various accuracy indicators. According to the hierarchical detection coordinate system, these data are labeled to clearly indicate the position of each data point in the coordinate system. The labeled data is organized into a data set format suitable for machine learning model training. The data set should include input features such as measurement values of various accuracy indicators and corresponding output labels such as positions in the hierarchical detection coordinate system or accuracy levels. A suitable supervised learning model is selected, such as support vector machines, random forests, or neural networks. The constructed training data set is used to train the model, enabling it to learn the mapping relationship from input features to output labels. The trained model is evaluated using a validation set to check its prediction accuracy and generalization ability. Based on the evaluation results, the model is adjusted and optimized, such as adjusting model parameters, changing model structure, or trying different algorithms.

[0020] S4: Based on the spatio-temporal characteristics, laser scanning and integration of the target drill bit are performed to determine the spatio-temporal detection point cloud.

[0021] Specifically, the target drill bit is fixed in the working area of the scanner, ensuring stable light in the scanning environment and avoiding interference from external light sources. A laser scanner is used to scan the drill bit 360 degrees to obtain three-dimensional data of the drill bit surface. The drill bit can be scanned multiple times at different time points, and the data obtained from multiple scans is integrated to form a unified spatio-temporal point cloud data set. Registration algorithms such as ICP are used to align the scanning data at different time points to the same coordinate system. The spatio-temporal point cloud data is preprocessed for denoising and smoothing to improve data accuracy and readability. Data compression algorithms are used to compress the spatio-temporal point cloud data to reduce data volume and improve processing efficiency.

[0022] S5: Combine signal processing models to correct the bias of the spatio-temporal detection point cloud and enhance the local features, and determine the distributed surface features, where the feature enhancement processing method includes joint fitting and algorithm processing.

[0023] Specifically, the point cloud data is analyzed to identify systematic errors caused by scanning equipment or environmental factors. Geometric transformations or mathematical models are used to correct the point cloud data, eliminating or reducing bias. For example, affine transformations or nonlinear transformations can be used to adjust the position and orientation of the point cloud. The point cloud is segmented into multiple local regions, and within each local region, surface features such as edges, corners, concavities, and convexities are extracted. Algorithms are applied to enhance these features, making them more prominent. For example, a high-pass filter can be used to enhance edge features, or morphological operations can be used to enhance specific geometric shapes. Scan data from different time points or different perspectives is fused. Fitting algorithms such as least squares are used to smooth and integrate the data, and based on the fitting results, the point cloud data is optimized, such as removing outliers and filling in missing data. Machine learning or deep learning algorithms are used to identify, classify, and label different surface features from the enhanced point cloud data.

[0024] S6: Based on the precision detection module, the state evaluation and assembly analysis of the distributed surface features are performed, and the precision detection single column is determined and visualized displayed, wherein the analysis standard includes single point analysis and time series trend analysis.

[0025] Specifically, the extracted surface features are classified as qualified or unqualified. Each feature is quantitatively evaluated using specific numerical indicators such as roughness, shape error, etc. to describe its state. Based on the evaluation results of all features, the overall surface precision of the drill bit is comprehensively evaluated. The mutual influence between each feature and their influence on the overall precision is analyzed. Detailed analysis is performed on individual feature points or local regions to identify possible problem points or abnormalities. For the identified problem points, possible causes such as material problems, improper processing parameters, etc. are analyzed. Time series analysis is performed on multiple scan data to monitor the trend of surface features over time, and based on historical data, future possible precision changes are predicted. The results of state evaluation and assembly analysis are recorded in the detection single column. Three-dimensional visualization technology is used to visually display the distributed surface features and detection results on the terminal interface.

[0026] Further, as shown in Figure 2 , the step S2 of the present application further comprises: determining an arbitrary element group across layers, the element group being feature-index, index-precision; based on the element group, combining relevant calculation formulas, performing correlation calculation of inter-layer elements to determine a correlation coefficient set; determining a one-layer coordinate system with precision as the origin and multiple indexes as the coordinate axes, determining a two-layer coordinate system with index as the origin and multiple features as the coordinate axes, the two-layer coordinate system including multiple; fusing the one-layer coordinate system and the two-layer coordinate system to determine the hierarchical detection coordinate system.

[0027] Specifically, the specific features of the feature layer, such as surface texture, corrugation, etc., are associated with the quantitative indicators of the indicator layer, such as roughness, shape error, etc., to form a feature-indicator element group. The quantitative indicators of the indicator layer are associated with the final polishing precision requirements to form an indicator-precision element group. The correlation between the feature-indicator element group and the indicator-precision element group is calculated using a correlation calculation formula. A set of correlation coefficients is determined, which reflect the mutual relationship and influence between different levels of elements. A one-level coordinate system is established with precision as the origin and the multiple indicators of the indicator layer as the coordinate axes. This coordinate system can be represented as a point in a multi-dimensional space, with each dimension corresponding to an indicator. A two-level coordinate system is established for each indicator with the multiple features of the feature layer as the coordinate axes. In this way, each indicator has an independent coordinate system for describing the features related to it. The one-level coordinate system and the two-level coordinate system are fused to form a comprehensive hierarchical detection coordinate system. This coordinate system integrates the information of the three levels of precision, indicators and features, providing a comprehensive reference framework. In this coordinate system, the influence of different elements on the overall precision can be analyzed and evaluated through level conversion and coordinate point movement.

[0028] Further, the present application also includes: The correlation calculation formula is obtained: ; Wherein, is the correlation coefficient of the element group, , is the element group for correlation calculation, is the total number of samples of the element group, and the sample , has a one-to-one correspondence; is the th sample, is the th sample, is the sample mean of the element , is the sample mean of the element ; wherein the sample acquisition standard is continuous samples under time sequence polishing.

[0029] Specifically, for continuous samples under time sequence polishing, the same drill bit or multiple drill bits are measured multiple times at different time points to capture the changes in the polishing process. For example, the correlation coefficient between roughness and surface texture is calculated, and a series of measurement data is collected, each data point containing the values of the two elements. Then, these data points are substituted into the above formula to calculate the correlation coefficient.

[0030] Further, the present application also includes: identifying a surface geometry distribution of the target drill bit, setting pre-detection points based on a preset step length, wherein the pre-detection points include key detection points and step length setting points; determining a scanning trajectory based on the pre-detection points, determining scanning parameters based on material properties of the target drill bit; controlling a laser scanner to perform scanning detection on the target drill bit based on the scanning trajectory and the scanning parameters.

[0031] Specifically, according to the feedback signal of the laser point cloud, the features and other information can be determined by analysis, and the geometric structure can be analyzed according to the point cloud distribution. Key detection points are set on the key geometric features of the drill bit, which are the key areas of the surface quality, such as the edge of the blade. Based on the preset step length, a series of detection points are uniformly set along the surface of the drill bit. The selection of the step length depends on the required detection accuracy and scanning time. According to the position of the pre-detection points, the moving trajectory of the laser scanner is planned to ensure that the scanning trajectory covers all the pre-detection points and can maintain stable scanning speed and direction during scanning. The material properties of the target drill bit include reflectivity, surface roughness, etc., which will affect the effect of laser scanning. According to the material properties, the parameters of the laser scanner are adjusted, including laser power, scanning speed, resolution, etc. Using scanning control software or automatic control system, the laser scanner is controlled according to the scanning trajectory and parameters. Perform scanning detection and collect data of the target drill bit surface.

[0032] Further, the present application also includes: determining a multi-level step length, training a measurement point distributor; assisting the measurement point distributor to randomly determine a plurality of initial detection points based on the surface geometry distribution, wherein the initial detection points meet the dispersion requirement; randomly determining any step length level based on the multi-level step length, and performing point expansion and progressive iteration on any initial detection point until the surface coverage is met to determine the pre-detection points; wherein there is a pre-detection point difference frequency distribution based on time nodes under time characteristics.

[0033] Specifically, according to the surface geometry distribution of the drill bit and the detection requirements, a plurality of different step levels are determined. Each step level represents different detection accuracy, the smaller the step, the higher the detection accuracy, and a measurement point distributor model is trained using machine learning or deep learning technology. The model will learn how to generate initial detection points with specific dispersion requirements based on the surface geometry distribution of the drill bit. Using the trained measurement point distributor, a plurality of initial detection points are randomly determined based on the surface geometry distribution of the drill bit. These initial detection points should meet the dispersion requirements to ensure coverage of all key areas of the drill bit. Starting from any initial detection point, the point is expanded according to the current step level, and if the detection points of the current step level do not cover all key areas, the step level is increased and the point expansion is performed again. Repeat this process until the surface coverage requirement is met. At each step level, the pre-detection points are finally determined, which will be used for subsequent laser scanning and accuracy detection. Under the time characteristic, the pre-detection points will be distributed according to the time node. At different time points, the distribution of pre-detection points will be different to adapt to the surface state of the drill bit at different processing stages.

[0034] Further, the step S5 of the present application further comprises: Mining the signal defects of historical laser detection, determining a preset processing mode, wherein each processing mode is identified with a parallel processing amount; based on the preset processing mode, performing independent training of the processing block to determine a plurality of signal processing blocks; integrating the plurality of signal processing blocks, allocating operator resources based on the parallel processing amount, and generating the signal processing model.

[0035] Specifically, collect and organize the signal data of past laser detection, including all known signal defects and abnormal situations, analyze these data, and identify common signal defect patterns and features. According to the mined signal defects, define different preset processing modes, each processing mode corresponds to a specific signal defect type, and identifies the amount of parallel processing. Based on the preset processing mode, each processing mode is independently trained to identify and process the corresponding signal defect. Train through machine learning algorithms such as deep learning, support vector machine, etc., to improve the accuracy and efficiency of signal processing. Integrate the trained processing blocks into a signal processing model. According to the preset parallel processing amount, allocate operator resources to optimize the running efficiency and processing capacity of the model. Use unlabeled data or cross-validation methods to verify the performance of the signal processing model, and optimize the model according to the verification results, including adjusting parameters, adding or reducing processing blocks, etc.

[0036] Further, the present application further comprises: Based on the precision detection module, the distributed surface features are mapped and measured with respect to the baseline coordinates, and the feature coordinate distribution is determined based on the two-layer coordinate system. The feature detection coefficient is measured based on the hierarchical mapping of the hierarchical detection coordinate system and the hierarchical correlation coefficient. The index detection coefficient is determined by a first assembly calculation of the feature detection coefficient. The index detection coefficient is calculated by a second assembly calculation, and the assembly detection coefficient is determined. The assembly analysis includes the assembly of each detection point and the spatial globality.

[0037] Specifically, the coordinates of each distributed surface feature are positioned using a two-layer coordinate system. These coordinate positions reflect the spatial distribution and relative position of the features on the drill bit surface. The feature coordinates are mapped with the baseline coordinates to determine the actual position and relative position of the features, and the feature coordinates and baseline coordinates are measured by difference to calculate the deviation of the features relative to the baseline, and the feature detection coefficient is measured. The coefficient reflects the difference between the feature and the preset standard. The feature detection coefficient is associated with the coordinates of the index layer and the feature layer using the hierarchical mapping of the hierarchical detection coordinate system. The feature detection coefficient is calculated by a first assembly calculation combined with the hierarchical correlation coefficient to obtain the index detection coefficient. The index detection coefficient reflects the relationship between the feature and the index layer. The index detection coefficient is calculated by a second assembly calculation to obtain the assembly detection coefficient. The assembly detection coefficient is the comprehensive result of the feature detection coefficient, the index detection coefficient and the hierarchical correlation coefficient, which reflects the final difference between the feature and the preset standard. The assembly analysis is performed on each detection point to evaluate its deviation from the preset standard. The spatial globality analysis is performed to ensure that the detection points in each region of the drill bit surface meet the preset standard.

[0038] Further, the present application also includes: Based on the detection step, the detection link is determined; according to the detection link, the data dimension and the coefficient dimension are integrated by link, and the mapping link group is determined; the results of the mapping link group are calibrated, and added to the precision detection single column.

[0039] Specifically, all steps related to drill bit surface grinding precision detection are identified and defined, including laser scanning, signal processing, feature extraction, precision evaluation, etc. A complete detection link is constructed to describe the entire process from data acquisition to result output. Ensure that each step is closely connected to the subsequent steps to form a coherent process. Determine the position and relationship of data dimension and coefficient dimension in the detection link, construct the mapping link group, and integrate the data dimension and the coefficient dimension by link. The results of each detection step are calibrated, including error analysis, correction processing, quality control, etc. The calibrated results are added to the precision detection single column, which contains all key information such as detection time, detection personnel, detection equipment, detection results, etc.

[0040] In summary, the hierarchical drill bit polishing state detection and evaluation method provided by the application has the following technical effects: By determining the preset polishing precision based on the machining standard of the target drill bit, and performing hierarchical dissection of the precision elements, a cascaded precision system is obtained, wherein the dissection levels at least include an index layer and a feature layer; the cascaded precision system is traversed to calculate the level correlation coefficient and perform hierarchical multivariate coordinate conversion, the baseline coordinates of the preset polishing precision are distributed, and a hierarchical detection coordinate system is determined; based on the hierarchical detection coordinate system, the precision detection module is supervised and trained; based on the space-time characteristics, the target drill bit is subjected to laser scanning and integration to determine the space-time detection point cloud; in combination with the signal processing model, the space-time detection point cloud is subjected to bias correction and local feature enhancement processing to determine the distributed surface features, wherein the feature enhancement processing mode includes joint inspection fitting and algorithm processing; based on the precision detection module, the distributed surface features are subjected to state evaluation and assembly analysis to determine the precision detection single column and perform terminal visual display, wherein the analysis standard includes single-point analysis and time series trend analysis, which effectively solves the technical problem that the drilling bit surface polishing quality is unstable due to the limited artificial detection precision in the prior art, further affecting the drilling precision and production efficiency, effectively performs bias correction and local feature enhancement processing on the space-time detection point cloud, thereby improving the recognition degree and precision of the surface features, and optimizes the production process and improves the product quality.

[0041] The above description of disclosed embodiments enables one of ordinary skill in the art to make or use the application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein can be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0042] Obviously, those skilled in the art can make various modifications and variations to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalent technologies, the present application also intends to include these modifications and variations.

Claims

1. A hierarchical method for detecting and evaluating the grinding status of drill bits, characterized in that, The method includes: Based on the processing standards of the target drill bit, the preset grinding accuracy is determined, and the accuracy elements are hierarchically dissected to form a cascaded accuracy system. The dissection hierarchy includes at least an index layer and a feature layer. Traverse the cascaded precision system, calculate the hierarchical correlation coefficient, perform hierarchical multivariate coordinate transformation, distribute the baseline coordinates of the preset grinding precision, and determine the hierarchical detection coordinate system; Based on the aforementioned hierarchical detection coordinate system, a supervised training accuracy detection module is established. Based on spatiotemporal characteristics, the target drill bit is laser scanned and integrated to determine the spatiotemporal detection point cloud; By combining a signal processing model, bias correction and local feature enhancement processing are performed on the spatiotemporal detection point cloud to determine distributed surface features. The feature enhancement processing methods include joint detection fitting and algorithm processing. Based on the accuracy detection module, the distributed surface features are evaluated for status and analyzed as a whole. The accuracy detection column is determined and displayed on the terminal. The analysis criteria include single-point analysis and time-series trend analysis.

2. The hierarchical drill bit grinding status detection and evaluation method as described in claim 1, characterized in that, The determination of the hierarchical detection coordinate system includes: Determine any group of features across layers, where the group of features is a feature-indicator or an indicator-precision. Based on the aforementioned element group and in conjunction with relevant calculation formulas, the correlation between inter-layer elements is calculated to determine the correlation coefficient set. A first-level coordinate system is determined with accuracy as the origin and multiple indicators as the coordinate axes. A second-level coordinate system is determined with indicators as the origin and multiple features as the coordinate axes. The second-level coordinate system includes multiple systems. The first-level coordinate system and the second-level coordinate system are merged to determine the hierarchical detection coordinate system.

3. The hierarchical drill bit grinding condition detection and evaluation method as described in claim 2, characterized in that, The method includes: Obtain the relevant calculation formula: ； in, The correlation coefficient of the feature group. , For the element group used to perform correlation calculations, The total number of samples for the feature group. , There is a one-to-one correspondence; For the first item sample, For the first item sample, as elements The sample mean, as elements The sample mean; The standard for obtaining samples is continuous samples under time-series polishing.

4. The hierarchical drill bit grinding status detection and evaluation method as described in claim 1, characterized in that, Before performing laser scanning on the target drill bit, the following steps are included: Identify the surface geometry of the target drill bit and set pre-detection points based on a preset step size, wherein the pre-detection points include key detection points and step size setting points; Based on the pre-detection points, the scanning trajectory is determined, and based on the material properties of the target drill bit, the scanning parameters are determined. Based on the scanning trajectory and the scanning parameters, the laser scanner is controlled to scan and detect the target drill bit.

5. The hierarchical drill bit grinding condition detection and evaluation method as described in claim 4, characterized in that, The setting of pre-detection points based on a preset step size includes: Determine multi-level step sizes and train the test point distributor; The measuring point distributor is used to randomly determine multiple initial detection points based on the surface geometry, wherein the initial detection points meet the dispersion requirement; Based on the multi-level step size, any step size level is randomly determined, and point expansion and progressive iteration are performed on any initial detection point until the surface coverage is satisfied, and the pre-detection point is determined. Among them, under the time characteristic, there is a difference frequency distribution of pre-detection points based on time nodes.

6. The hierarchical drill bit grinding condition detection and evaluation method as described in claim 1, characterized in that, Constructing a signal processing model includes: By identifying signal defects from historical laser detection, preset processing modes are determined, with each processing mode indicating the amount of parallel processing. Based on the preset processing mode, independent training of processing blocks is performed to determine multiple signal processing blocks; The signal processing model is generated by integrating the multiple signal processing blocks and allocating operator resources based on the parallel processing volume.

7. The hierarchical drill bit grinding condition detection and evaluation method as described in claim 2, characterized in that, The analysis criteria include single-point analysis, which is a precision analysis of the polishing results, including: Based on the accuracy detection module, the distributed surface features are distributed according to the two-layer coordinate system. Based on the baseline coordinates, the feature coordinates are mapped and differentially measured to measure the feature detection coefficients. Based on the hierarchical mapping of the hierarchical detection coordinate system and combined with the hierarchical correlation coefficient, the feature detection coefficients are calculated in one step to determine the index detection coefficients. Based on the hierarchical correlation coefficient, a second overall calculation is performed on the indicator detection coefficient to determine the overall detection coefficient; Among them, the assembly analysis includes the assembly of each detection point and the global spatial aspect.

8. The hierarchical drill bit grinding condition detection and evaluation method as described in claim 7, characterized in that, include: Based on the detection steps, the detection chain is determined; Based on the detection link, the data dimension and coefficient dimension are integrated into a link-based manner to determine the mapping link group; The results of the mapping link group are calibrated and added to the accuracy detection column.

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