Performance detection data processing and analysis method and system for denture engraving machine

Abstract

The present invention discloses a performance detection data processing and analysis method and system for a denture engraving machine, and relates to the technical field of electrical digital data processing. The performance detection data processing and analysis method for a denture engraving machine comprises the following steps: detection interference judgment, detection evaluation and detection adjustment. The present invention obtains a detection interference index through the acquired detection interference data and determines whether to perform detection evaluation. If detection evaluation is performed, a viewing angle evaluation index is obtained based on the acquired viewing angle data and the detection interference index to determine whether to perform viewing angle adjustment. At the same time, an occlusion evaluation index is obtained based on the acquired occlusion data and the detection interference index to determine whether to perform occlusion adjustment. Finally, it is determined whether to perform detection adjustment based on the viewing angle evaluation index and the occlusion evaluation index, thereby achieving the effect of improving the accuracy of obtaining the performance detection data of the denture engraving machine, and solving the problem of inaccurate performance detection data acquisition of the denture engraving machine in the prior art.

Description

Technical Field

[0001] The invention relates to the technical field of electric digital data processing, and in particular to a performance detection data processing and analysis method and system for a denture engraving machine. Background Art

[0002] With the growing demand for digital dental care and customized dentures, denture engraving machines are increasingly being used in the dental manufacturing field. As a precision processing equipment, denture engraving machines use tiny, high-speed rotating cutters to perform fine engravings on materials such as ceramics and resins to meet the high fit requirements of dentures. However, to ensure that the processed dentures have the accuracy and shape that meet the standards, performance testing becomes a key link. This performance testing system requires not only accurate data collection and efficient data processing, but also the use of multivariate analysis methods to evaluate multiple indicators such as engraving accuracy, tool wear, and machine tool stability to ensure the final quality of the dentures.

[0003] Existing performance detection technologies for denture engraving machines mainly rely on high-precision sensors, three-dimensional scanning and image processing technologies to achieve real-time monitoring of engraving accuracy, tool wear and surface quality. Usually, the system collects key data in the engraving process through vibration sensors, torque sensors and three-dimensional point cloud scanning devices, and uses denoising algorithms and feature extraction methods to perform preliminary data processing. Subsequently, error analysis, spectrum analysis and other methods are used to evaluate the processing accuracy, detect the stability of the system, and generate feedback signals to adjust the processing parameters of the equipment. However, existing technologies have limitations in data processing speed and analysis accuracy, especially in terms of multi-sensor data synchronization, complex surface feature analysis and personalized optimization. It is difficult to meet the requirements of high precision and high efficiency in denture processing, and further optimization and improvement are urgently needed.

[0004] In the prior art, for complex structures such as dentures, some areas may not be fully captured due to the fixed position of the equipment or the influence of the light source, resulting in inaccurate denture feature extraction and inaccurate performance test data acquisition of the denture engraving machine. Summary of the invention

[0005] The embodiments of the present application solve the problem of inaccurate acquisition of performance test data of denture engraving machines in the prior art by providing a performance test data processing and analysis method and system for denture engraving machines, thereby improving the accuracy of acquisition of performance test data of denture engraving machines.

[0006] An embodiment of the present application provides a performance detection data processing and analysis method for a denture engraving machine, comprising the following steps: obtaining a detection interference index by acquiring detection interference data of an area to be detected, and judging whether to perform a detection evaluation based on the detection interference index, wherein the detection interference index is used to comprehensively quantify the degree to which the detection process is affected by interference; if a detection evaluation is performed, judging whether to perform a viewing angle adjustment by obtaining a viewing angle evaluation index based on the acquired viewing angle data and the detection interference index, and judging whether to perform an occlusion adjustment by obtaining an occlusion evaluation index based on the acquired occlusion data and the detection interference index, wherein the viewing angle evaluation index is used to quantify the degree to which the viewing angle affects the detection, and the occlusion evaluation index is used to quantify the degree to which the occlusion affects the detection; judging whether to perform a detection adjustment according to the viewing angle evaluation index and the occlusion evaluation index.

[0007] Furthermore, the detected interference data includes depth gradient, normal vector change rate, normal vector standard deviation, reprojection error and reflectivity; the viewing angle data includes point cloud density, viewing angle coverage and scanning angle; the occlusion data includes grayscale mean, color contrast and missing data point ratio.

[0008] Furthermore, the specific acquisition process of the detection interference index is as follows: number the detection area and obtain the detection interference data; obtain reference detection interference data from a preset database, the reference detection interference data including the maximum limit of the depth gradient, the maximum limit of the normal vector change rate, the maximum limit of the reprojection error and the minimum limit of the reflectivity; obtain the detection interference index according to the detection interference data and the reference detection interference data, and obtain the detection interference index based on the detection interference index, the detection interference index including the depth index, the normal vector index, the reprojection index and the reflection index.

[0009] Furthermore, the specific acquisition process of the perspective evaluation index is as follows: obtain perspective data and detection interference index; obtain reference perspective data from a preset database, the reference perspective data including a minimum limit value of point cloud density, a minimum limit value of perspective coverage and a scanning angle reference interval, the scanning angle reference interval including a minimum scanning angle and a maximum scanning angle; obtain a perspective weight from a preset database, the perspective weight is used to quantify the influence of the detection interference index on the perspective evaluation index; obtain a scanning angle index based on the scanning angle and the scanning angle reference interval, the scanning angle index is used to quantify the influence of the scanning angle on data acquisition; process the point cloud density, the perspective coverage, the minimum limit value of the point cloud density, the minimum limit value of the perspective coverage, the scanning angle index, the perspective weight and the detection interference index to obtain the perspective evaluation index.

[0010] Furthermore, the specific acquisition process of the occlusion evaluation index is as follows: obtain occlusion data and detection interference index and obtain reference occlusion data from a preset database, wherein the reference occlusion data includes a minimum grayscale mean value and a minimum color contrast value; obtain occlusion weight from the preset database, wherein the occlusion weight is used to quantify the influence of the detection interference index on the occlusion evaluation index; process the occlusion data, the reference occlusion data, the occlusion weight and the detection interference index to obtain the occlusion evaluation index;

[0011] The specific limiting expression of the occlusion evaluation index is as follows:

[0012] ;

[0013] In the formula, n represents the number of the area to be detected, , Indicates the total number of areas to be detected, represents the grayscale mean of the nth area to be detected, Indicates the color contrast of the nth area to be detected, Indicates the proportion of missing data points in the nth region to be detected, represents the minimum grayscale mean value, Indicates the minimum color contrast. represents the occlusion weight, represents the detection interference index of the nth area to be detected, represents the occlusion evaluation index of the nth area to be detected, and e represents a natural constant.

[0014] An embodiment of the present application provides a performance detection data processing and analysis system for a denture engraving machine, including a detection interference judgment module, a detection evaluation module and a detection adjustment module; wherein the detection interference judgment module is used to obtain a detection interference index by acquiring detection interference data of an area to be detected, and judge whether to perform detection evaluation based on the detection interference index, and the detection interference index is used to comprehensively quantify the degree to which the detection process is affected by interference; the detection evaluation module is used to determine whether to perform a perspective adjustment based on a perspective evaluation index obtained from the acquired perspective data and the detection interference index if a detection evaluation is performed, and at the same time determine whether to perform an occlusion adjustment based on an occlusion evaluation index obtained from the acquired occlusion data and the detection interference index, and the perspective evaluation index is used to quantify the degree to which the perspective affects the detection, and the occlusion evaluation index is used to quantify the degree to which the occlusion affects the detection; the detection adjustment module is used to determine whether to perform detection adjustment based on the perspective evaluation index and the occlusion evaluation index.

[0015] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0016] 1. A detection interference index is obtained through the acquired detection interference data and it is determined whether to perform detection evaluation. If detection evaluation is performed, a viewing angle evaluation index is obtained based on the acquired viewing angle data and the detection interference index to determine whether to perform viewing angle adjustment. At the same time, an occlusion evaluation index is obtained based on the acquired occlusion data and the detection interference index to determine whether to perform occlusion adjustment. Finally, it is determined whether to perform detection adjustment based on the viewing angle evaluation index and the occlusion evaluation index, so as to more accurately and timely adjust the detection equipment, thereby improving the accuracy of obtaining the performance detection data of the denture engraving machine, and effectively solving the problem of inaccurate performance detection data acquisition of the denture engraving machine in the prior art.

[0017] 2. By numbering the areas to be detected and obtaining detection interference data, and then obtaining reference detection interference data from the preset database, and then obtaining the detection interference index based on the detection interference data and the reference detection interference data, and finally obtaining the detection interference index based on the detection interference index, the degree to which the performance detection process is affected by interference is accurately quantified, thereby achieving more real-time control of the detection equipment to extract more accurate feature data.

[0018] 3. The perspective evaluation index is obtained by processing the acquired perspective data and detection interference index as well as the reference perspective data and perspective weight obtained from the preset database, so as to more finely evaluate the impact of perspective on data detection, and then take corresponding measures in time to extract more accurate feature data. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A flow chart of a method for processing and analyzing performance detection data of a denture engraving machine provided in an embodiment of the present application;

[0020] Figure 2 A schematic diagram of the depth index according to the depth gradient provided in the embodiment of the present application;

[0021] Figure 3 A schematic diagram of the change of the normal vector index with the normal vector change rate provided in the embodiment of the present application;

[0022] Figure 4 A schematic diagram of the reprojection index according to the embodiment of the present application changing with the reprojection error;

[0023] Figure 5 A schematic diagram of the reflection index according to the reflectivity provided in the embodiment of the present application. DETAILED DESCRIPTION

[0024] The embodiments of the present application solve the problem of inaccurate acquisition of performance detection data of denture engraving machines in the prior art by providing a performance detection data processing and analysis method and system for denture engraving machines. The method numberes the detection areas and obtains detection interference data, and then obtains reference detection interference data from a preset database. Then, a detection interference index is obtained based on the detection interference data and the reference detection interference data. Then, a detection interference index is obtained based on the detection interference index and it is determined whether to perform a detection evaluation. If a detection evaluation is performed, a perspective evaluation index is obtained by processing the obtained perspective data and the detection interference index as well as the reference perspective data and the perspective weight obtained from the preset database to determine whether to perform a perspective adjustment. At the same time, an occlusion evaluation index is obtained based on the obtained occlusion data and the detection interference index to determine whether to perform an occlusion adjustment. Finally, it is determined whether to perform a detection adjustment based on the perspective evaluation index and the occlusion evaluation index, thereby improving the accuracy of acquiring the performance detection data of the denture engraving machine.

[0025] The technical solution in the embodiment of the present application is to solve the problem of inaccurate acquisition of performance test data of the above-mentioned denture engraving machine, and the overall idea is as follows:

[0026] Whether to perform detection evaluation is determined by the acquired detection interference index. If detection evaluation is performed, whether to perform viewing angle adjustment is determined based on the acquired viewing angle data and the detection interference index. At the same time, whether to perform occlusion adjustment is determined based on the acquired occlusion data and the detection interference index to obtain the occlusion evaluation index. Finally, whether to perform detection adjustment is determined based on the viewing angle evaluation index and the occlusion evaluation index, thereby achieving the effect of improving the accuracy of obtaining performance detection data of the denture engraving machine.

[0027] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0028] like Figure 1 As shown, it is a flow chart of a performance detection data processing and analysis method for a denture engraving machine provided in an embodiment of the present application, the method comprising the following steps: obtaining a detection interference index by acquiring detection interference data of the area to be detected, judging whether to perform detection evaluation based on the detection interference index, the detection interference index being used to comprehensively quantify the degree to which the detection process is affected by interference; if detection evaluation is performed, judging whether to perform a viewing angle adjustment based on a viewing angle evaluation index obtained based on the acquired viewing angle data and the detection interference index, and judging whether to perform an occlusion adjustment based on an occlusion evaluation index obtained based on the acquired occlusion data and the detection interference index, the viewing angle evaluation index being used to quantify the degree to which the viewing angle affects the detection, and the occlusion evaluation index being used to quantify the degree to which the occlusion affects the detection; judging whether to perform detection adjustment based on the viewing angle evaluation index and the occlusion evaluation index, the detection adjustment being used to reduce the influence of interference factors on data acquisition.

[0029] In this embodiment, the area to be inspected represents an area with a scanning device, and the scanning device obtains surface features of the denture engraving machine by scanning to extract feature data, wherein the feature data includes but is not limited to surface geometry data (curvature of the denture surface), texture feature data (surface roughness), color and optical properties (gloss, color data), size and distance data, etc.; the scanning device generally includes but is not limited to lidar, 3D scanner, solid-state lidar and depth camera; based on the analysis of detection interference index, viewing angle evaluation index and occlusion evaluation index, it can not only improve the detection accuracy and efficiency, but also reduce the detection cost, improve the intelligence, stability and adaptability of the denture engraving machine to complex environments, and also improve the accuracy of obtaining performance detection data of the denture engraving machine.

[0030] It should be added that the detection interference data includes depth gradient, normal vector change rate, reprojection error and reflectivity; the viewing angle data includes point cloud density, viewing angle coverage and scanning angle; the occlusion data includes grayscale mean, color contrast and proportion of missing data points.

[0031] In this embodiment, the depth gradient and reflectivity are directly obtained by the depth camera; the normal vector change rate is determined by a point cloud normal vector estimation algorithm (such as PCA, Principal Component Analysis or SVD, Singular Value Decomposition) to determine the normal vector of each data point, and calculate the angle difference between the normal vectors of adjacent data points, and the average value of these angle differences is used as the normal vector change rate, where the data point represents the data point at the center of the local area in the point cloud normal vector estimation algorithm; the reprojection error represents the deviation after the point cloud is reprojected to the image plane, by reprojecting the three-dimensional point to the two-dimensional image plane, and calculating the distance difference between the two-dimensional pixel point and the reprojection point, the average value of these distance differences is the reprojection error; the point cloud density represents the number of points per unit area and is used to evaluate the integrity of the viewing angle coverage, by dividing the area to be detected into multiple small grids (for example, 1 cm x 1cm), then count the number of points in each grid and calculate the average point density of all grids; the viewing angle coverage rate indicates the percentage of the area to be detected covered by the current viewing angle. First, the total area of ​​the area to be detected is calculated, then the area of ​​the area to be detected covered within the viewing angle is calculated, and then the covered area and the total area are ratioed to obtain the viewing angle coverage rate; the scanning angle is obtained by real-time monitoring of the rotation angle of the camera; the grayscale mean is obtained through the grayscale image of the depth camera, and the grayscale values ​​of the area to be detected are accumulated and averaged to obtain the grayscale mean; the color contrast can be obtained through the RGB camera; the proportion of missing data points indicates the proportion of data points whose depth values ​​cannot be measured due to occlusion. The proportion of missing data points is obtained by ratioing the number of data points whose depth values ​​cannot be measured in the statistical point cloud with the total number of data points; the above method is used to obtain data related to detection interference, viewing angle and occlusion, which provides data support for subsequent data analysis.

[0032] Furthermore, the specific process of obtaining the detection interference index is as follows: numbering the detection area and obtaining the detection interference data; obtaining the reference detection interference data from the preset database, the reference detection interference data including the maximum limit of the depth gradient, the maximum limit of the normal vector change rate, the maximum limit of the reprojection error and the minimum limit of the reflectivity; obtaining the detection interference index according to the detection interference data and the reference detection interference data, and obtaining the detection interference index based on the detection interference index, the detection interference index including the depth index (i.e. ), the normal vector index (i.e. ), reprojection index (i.e. ) and reflection indicators (i.e. );

[0033] The specific limiting expression of the detection interference index is as follows:

[0034] ;

[0035] ;

[0036] ;

[0037] ;

[0038] ;

[0039] In the formula, n represents the number of the area to be detected, , Indicates the total number of areas to be detected, represents the depth gradient of the nth area to be detected, Represents the rate of change of the normal vector of the nth area to be detected, represents the reprojection error of the nth region to be detected, represents the reflectivity of the nth area to be detected, represents the maximum limit of depth gradient, represents the maximum limit of the rate of change of the normal vector, represents the maximum limit of reprojection error, Represents the minimum reflectivity limit, Represents the depth index of the nth area to be detected, Represents the normal vector index of the nth area to be detected, Represents the reprojection index of the nth area to be detected, Represents the reflection index of the nth area to be detected, Represents the detection interference index of the nth area to be detected.

[0040] In this embodiment, the algorithm combines the depth index (i.e. ), the normal vector index (i.e. ), reprojection index (i.e. ) and reflection indicators (i.e. ) Comprehensive analysis is performed to obtain the detection interference index, and the depth index, normal vector index, reprojection index and reflection index are all positively correlated with the detection interference index. With the increase of the depth index, normal vector index, reprojection index and reflection index, the detection interference index also increases, indicating that the area to be detected is more affected by interference when extracting data for performance testing. The analysis of the detection interference index is helpful to monitor in real time the degree of interference in the process of acquiring performance test data of the denture engraving machine, so as to take corresponding measures to timely reduce the interference effect on the acquisition of performance test data of the denture engraving machine.

[0041] Specifically, the value ranges of depth index, normal vector index, reprojection index and reflection index are all , the data change table of the detection interference index can be obtained, as shown in Table 1:

[0042] Table 1 Data change table of detection interference index

[0043]

[0044] It can be seen from Table 1 that when the depth index (i.e. ), the normal vector index (i.e. ), reprojection index (i.e. ) and reflection indicators (i.e. ) increase at the same time, the detection interference index also increases accordingly. For example, the depth index in the table increases from 0.4 in the first row to 0.8 in the fifth row, the normal vector index increases from 0.4 in the first row to 0.7 in the fifth row, the reprojection index increases from 0.6 in the first row to 0.9 in the fifth row, and the reflection index increases from 0.5 in the first row to 0.9 in the fifth row. The corresponding detection interference index also increases from 0.475 in the first row to 0.825 in the fifth row, which further proves that the depth index, normal vector index, reprojection index and reflection index are positively correlated with the detection interference index. The depth index, normal vector index, reprojection index and reflection index can more accurately reflect the interference degree of the extracted feature data in the process of denture engraving machine performance detection.

[0045] Specifically, assuming that the maximum limit of the depth gradient is 1, the maximum limit of the normal vector change rate is 1, the maximum limit of the reprojection error is 1, and the minimum limit of the reflectivity is 0.2, such as Figure 2 As shown, Figure 2 A schematic diagram of the depth index varying with the depth gradient provided in an embodiment of the present application, wherein it is assumed that the normal vector index, the reprojection index, and the reflection index are all 0, and as the depth gradient approaches the maximum limit of the depth gradient, the value of the depth index increases accordingly. When the value of the depth gradient is greater than the maximum limit of the depth gradient 1, the value of the depth index reaches the maximum value of 1, indicating that the greater the depth gradient, the greater the possibility of being blocked and having a poor viewing angle during the detection process; Figure 3 As shown, Figure 3 A schematic diagram of the change of the normal vector index with the normal vector change rate provided in an embodiment of the present application, wherein it is assumed that the depth index, the reprojection index, and the reflection index are all 0, and as the normal vector change rate approaches the maximum limit of the normal vector change rate, the value of the normal vector index also increases accordingly. When the value of the normal vector change rate is greater than the maximum limit of the normal vector change rate of 1, the value of the normal vector index reaches the maximum value of 1, indicating that the greater the normal vector change rate, the greater the possibility of being blocked and the viewing angle being poor during the detection process; Figure 4 As shown, Figure 4A schematic diagram of the change of the reprojection index with the reprojection error provided in the embodiment of the present application, wherein it is assumed that the depth index, the normal vector index and the reflection index are all 0, and as the reprojection error approaches the maximum limit of the reprojection error, the value of the reprojection index also increases. When the value of the reprojection error is greater than the maximum limit of the reprojection error 1, the value of the reprojection index reaches the maximum value of 1, indicating that the larger the reprojection error, the greater the possibility of being blocked and the viewing angle being poor during the detection process; Figure 5 As shown, Figure 5 A schematic diagram of the change of reflection index with reflectivity provided in an embodiment of the present application, wherein it is assumed that the depth index, normal vector index and reprojection index are all 0, and as the reflectivity approaches the minimum reflectivity limit of 0.2, the value of the reflectivity index also increases accordingly. When the reflectivity value is less than the minimum reflectivity limit, the value of the reflectivity index reaches a maximum value of 1, indicating that the smaller the reflectivity, the greater the possibility of being blocked and having a poor viewing angle during the detection process; wherein, the depth gradient and the normal vector change rate both describe the geometric changes on the surface of the object, and an area with a larger depth gradient usually results in a higher normal vector change rate. In addition, the depth gradient may also result in a higher reprojection error; by separately analyzing the depth index, normal vector index, reprojection index and reflection index, it is helpful to more accurately evaluate the degree of influence of interference during the detection process.

[0046] Specifically, the reference detection interference data is obtained from a preset database. The preset database is a database for storing various types of setting data established before the construction of the performance detection data processing and analysis method for the denture engraving machine. The database includes but is not limited to reference detection interference data, reference viewing angle data, reference occlusion data, viewing angle weight, occlusion weight, detection threshold, viewing angle threshold and occlusion threshold, wherein various values ​​are directly set by technical personnel. For example, in general, the reference detection interference data is obtained by performing mean operations on the detection interference data corresponding to the viewing angle problems and occlusion situations in the historical data to obtain the maximum limit of the depth gradient, the maximum limit of the normal vector change rate, the maximum limit of the reprojection error and the minimum limit of the reflectivity.

[0047] Furthermore, the specific process for determining whether to perform detection evaluation is as follows: compare the detection interference index with the detection threshold obtained from a preset database: if the detection interference index is less than the detection threshold, no detection evaluation is performed; if the detection interference index is not less than the detection threshold, the viewing angle data and occlusion data are obtained.

[0048] In this embodiment, the detection threshold is set to screen out situations where detection and evaluation are required. In these situations, further evaluation based on the viewing angle data and occlusion data can more accurately identify the causes of detection failure, thereby reducing false detections and missed detections and improving the detection accuracy and reliability of the denture engraving machine performance test.

[0049] Specifically, the detection threshold is obtained from a preset database. In a specific embodiment, the detection interference data corresponding to the detection evaluation situation in the historical data is substituted into the specific restriction expression of the detection interference index to obtain a detection interference index data set, and the result obtained by performing a mean operation on the detection interference index data set is recorded as the detection threshold.

[0050] Furthermore, the specific process of obtaining the perspective evaluation index is as follows: obtain perspective data and detection interference index; obtain reference perspective data from a preset database, the reference perspective data includes the minimum limit of point cloud density, the minimum limit of perspective coverage and the reference range of scanning angle, the scanning angle reference range includes the minimum value of scanning angle and the maximum value of scanning angle; obtain perspective weight from the preset database, the perspective weight is used to quantify the influence of detection interference index on perspective evaluation index; obtain scanning angle index based on scanning angle and scanning angle reference range, the scanning angle index is used to quantify the influence of scanning angle on data acquisition; process point cloud density, perspective coverage, minimum limit of point cloud density, minimum limit of perspective coverage, scanning angle index, perspective weight and detection interference index to obtain perspective evaluation index.

[0051] The specific limiting expression of the perspective evaluation index is as follows:

[0052] ;

[0053] ;

[0054] In the formula, n represents the number of the area to be detected, , Indicates the total number of areas to be detected, Represents the point cloud density of the nth area to be detected, represents the viewing angle coverage of the nth area to be detected, represents the scanning angle of the nth area to be detected, Indicates the minimum limit of point cloud density, Indicates the minimum viewing angle coverage, Indicates the minimum scanning angle. Indicates the maximum scanning angle. Represents the scanning angle index of the nth area to be detected, Indicates the perspective weight (value range 0-1), represents the detection interference index of the nth area to be detected, represents the viewing angle evaluation index of the nth area to be detected, and e represents a natural constant.

[0055] In this embodiment, the algorithm combines point cloud density, viewing angle coverage, minimum limit of point cloud density, minimum limit of viewing angle coverage, scanning angle index, viewing angle weight and detection interference index to obtain the viewing angle evaluation index, where the closer the point cloud density is to the minimum limit of point cloud density (i.e. The smaller the value of ), the larger the viewing angle evaluation index is, indicating that the possibility of data loss due to viewing angle problems is greater. At the same time, when the viewing angle coverage is closer to the minimum viewing angle coverage limit (i.e. The smaller the value of ), the larger the view angle evaluation index is, which means that the lower the view angle coverage is, the greater the possibility of incomplete feature data extraction in the area to be detected, and when the scanning angle index is smaller, the view angle evaluation index is also larger, which means that when the scanning angle deviates from the scanning angle reference interval, the quality of the extracted feature data is more likely to be worse; among them, a higher view angle coverage is often accompanied by a higher point cloud density, because the overlapping acquisition of multiple view angles increases the number of points in each area to be detected. When the scanning angle range is larger, that is, when the scanning device scans from multiple angles, the view angle coverage will also be higher; through a full analysis of the view angle data, the degree of interference of the view angle on the feature data process can be more accurately evaluated, so that corresponding measures can be taken to obtain the accuracy of the performance test data of the denture engraving machine.

[0056] Among them, the scanning angle index is obtained by comprehensive analysis of the scanning angle and the scanning angle reference interval. When the scanning angle is smaller than the minimum scanning angle or the scanning angle is larger than the maximum scanning angle, the value of the scanning angle index is smaller, which means that the more the scanning angle deviates from the scanning angle reference interval, the greater the possibility of difficulty in feature data extraction. Therefore, real-time monitoring of the scanning angle helps to adjust the scanning angle of the scanning device in time to obtain more accurate feature data.

[0057] Specifically, the reference viewing angle data is obtained from a preset database. In a specific embodiment, the viewing angle data corresponding to the poor viewing angle in the historical data is averaged to obtain the minimum limit of the point cloud density and the minimum limit of the viewing angle coverage. The scanning angle reference interval is obtained by referring to the device specification document, for example, by referring to the scanning angle reference interval of the depth camera model Intel RealSense D435i. .

[0058] Specifically, the perspective weight is the weight corresponding to the detection interference index when the perspective evaluation is performed in the preset database, which represents the numerical value of the influence of the detection interference index on the perspective evaluation index. When used, the weight corresponding to the detection interference index can be directly obtained from the preset database, and the corresponding relationship can be a pre-set mapping relationship. For example, when the perspective evaluation is performed, the detection interference index and the weight corresponding to the preset perspective evaluation index in the preset database form a mapping set, and the real-time detection interference data is input into the mapping set to obtain the corresponding weight, wherein the mapping relationship is a one-to-one correspondence. In this example, its value range is [0, 1].

[0059] Specifically, in this example, the sum of the viewing angle weight and the occlusion weight is 1.

[0060] Furthermore, the specific process for determining whether to perform perspective adjustment is as follows: compare the obtained perspective evaluation index with the perspective threshold obtained from a preset database: if the perspective evaluation index is less than the perspective threshold, the perspective adjustment is not performed; if the perspective evaluation index is not less than the perspective threshold, automatic path planning and switching of the wide-angle lens are performed. The automatic path planning is used to control the scanning device to automatically adjust the angle and distance around the denture. Switching the wide-angle lens means expanding the camera range through the wide-angle lens to make up for the shortcomings of a single perspective.

[0061] In this embodiment, by combining the perspective evaluation index, automatic path planning, and the judgment and execution strategy of switching the wide-angle lens, the automation and intelligence of the perspective adjustment are realized, which not only improves the detection coverage, accuracy and efficiency, but also reduces unnecessary operations and resource consumption, providing a flexible, reliable and efficient solution for real-time detection.

[0062] Specifically, the viewing angle threshold is obtained from a preset database. In a specific embodiment, the viewing angle data corresponding to the viewing angle problem in the historical data is substituted into the specific restriction expression of the viewing angle evaluation index to obtain a viewing angle evaluation index data set, and the result obtained by performing a mean operation on the viewing angle evaluation index data set is recorded as the viewing angle threshold.

[0063] Furthermore, the specific process of obtaining the occlusion evaluation index is as follows: obtaining occlusion data and a detection interference index and obtaining reference occlusion data from a preset database, the reference occlusion data including a minimum grayscale mean value and a minimum color contrast value; obtaining an occlusion weight from a preset database, the occlusion weight being used to quantify the degree of influence of the detection interference index on the occlusion evaluation index; processing the occlusion data, the reference occlusion data, the occlusion weight and the detection interference index to obtain the occlusion evaluation index;

[0064] The specific restriction expression of the occlusion evaluation index is as follows:

[0065] ;

[0066] In the formula, n represents the number of the area to be detected, , Indicates the total number of areas to be detected, represents the grayscale mean of the nth area to be detected, Indicates the color contrast of the nth area to be detected, Indicates the proportion of missing data points in the nth region to be detected, represents the minimum grayscale mean value, Indicates the minimum color contrast. represents the occlusion weight, represents the detection interference index of the nth area to be detected, represents the occlusion evaluation index of the nth area to be detected, and e represents a natural constant.

[0067] In this embodiment, the algorithm combines the occlusion data, the reference occlusion data, the occlusion weight and the detection interference index for comprehensive analysis to obtain the occlusion evaluation index, where the closer the grayscale mean is to the minimum grayscale mean (i.e. The smaller the value of ), the larger the occlusion evaluation index is, indicating that the possibility of data loss due to occlusion is greater. At the same time, when the color contrast is closer to the minimum color contrast (i.e. The smaller the value of ), the larger the occlusion evaluation index is, indicating that the lower the color contrast is, the greater the possibility of incomplete feature data extraction in the to-be-detected area is, and the smaller the proportion of missing data points is, the larger the occlusion evaluation index is, indicating that when the proportion of missing data points is larger, the extracted feature data may be more missing; among them, the grayscale mean affects the overall brightness level of the grayscale image, and the color contrast reflects the difference between the bright and dark parts. The areas with lower color contrast are usually accompanied by a higher proportion of missing data points, because in these areas it is difficult for scanning equipment to distinguish surface changes. The areas with higher color contrast are conducive to capturing details and reducing data point missing. Therefore, the grayscale mean, color contrast and the proportion of missing data points influence each other; through a full analysis of the occlusion data, the degree of interference of the occlusion aspect on the feature data process can be more accurately evaluated, so that corresponding measures can be taken to obtain the accuracy of the performance test data of the denture engraving machine.

[0068] Specifically, the reference occlusion data is obtained from a preset database. In a specific embodiment, the occlusion data corresponding to the occlusion situation in the historical data is averaged to obtain the minimum grayscale average value and the minimum color contrast value.

[0069] Furthermore, the specific process of determining whether to perform occlusion adjustment is as follows: compare the obtained occlusion evaluation index with the occlusion threshold obtained from the preset database: if the occlusion evaluation index is less than the occlusion threshold, no occlusion adjustment is performed; if the occlusion evaluation index is not less than the occlusion threshold, light source layout optimization and 3D scanning are performed; light source layout optimization means reducing shadows and dark areas caused by occlusion by adjusting the angle and intensity of the light source, and 3D scanning means starting the depth camera to rescan and extract feature data.

[0070] In this embodiment, the light source layout is optimized by adjusting the light source distance to reduce the diffusion of light and enabling the fill light to reduce the shadows caused by occlusion; through the combination of occlusion evaluation index, light source layout optimization and 3D scanning, the process can realize dynamic adjustment of occlusion, improve the accuracy, completeness and efficiency of detection, reduce resource waste and the risk of false detection, and improve the automation and robustness of performance detection, so that it can still work stably and accurately in complex occlusion scenarios.

[0071] Specifically, the occlusion threshold is obtained from a preset database. In a specific embodiment, the occlusion data corresponding to the occlusion situation in the historical data is substituted into the specific restriction expression of the occlusion evaluation index to obtain an occlusion evaluation index data set, and the result obtained by performing a mean operation on the occlusion evaluation index data set is recorded as the occlusion threshold.

[0072] Furthermore, the specific contents of judging whether to perform detection adjustment are as follows: if the viewing angle evaluation index is less than the viewing angle threshold and the occlusion evaluation index is less than the occlusion threshold, a button for executing detection adjustment measures is sent to the preset staff, otherwise the detection adjustment is not performed; the detection adjustment includes multi-view scanning and multi-axis control; multi-view scanning means adding scanning equipment to the area to be inspected to cover all angles of the denture surface; multi-axis control means adding a multi-axis robotic arm to enable the scanning equipment to move from various angles to capture the surface features of the denture.

[0073] In this embodiment, the staff is preset to realize multi-perspective scanning by adding scanning equipment to the area to be inspected, and deploy a multi-axis robotic arm to install the scanning equipment at the end of the robotic arm to realize mobile scanning, wherein the robotic arm can change the scanning angle, distance and position of the scanning equipment by movement; through the dual evaluation mechanism of perspective and occlusion, automatic notification and multi-perspective scanning and multi-axis control combined design, the intelligent and precise detection adjustment is realized, which not only improves the coverage, precision and automation level of detection, but also reduces resource consumption and the risk of false detection, providing support for the efficient and stable operation of performance detection in complex scenarios.

[0074] The performance detection data processing and analysis system for a denture engraving machine provided in an embodiment of the present application includes a detection interference judgment module, a detection evaluation module and a detection adjustment module; wherein the detection interference judgment module is used to obtain a detection interference index by acquiring detection interference data of an area to be detected, and judge whether to perform detection evaluation based on the detection interference index, and the detection interference index is used to comprehensively quantify the degree of influence of interference on the detection process; the detection evaluation module is used to determine whether to perform a perspective adjustment based on a perspective evaluation index obtained from the acquired perspective data and the detection interference index if a detection evaluation is performed, and at the same time, determine whether to perform an occlusion adjustment based on an occlusion evaluation index obtained from the acquired occlusion data and the detection interference index, the perspective evaluation index is used to quantify the degree of influence of the perspective on the detection, and the occlusion evaluation index is used to quantify the degree of influence of the occlusion on the detection; the detection adjustment module is used to determine whether to perform detection adjustment based on the perspective evaluation index and the occlusion evaluation index, and the detection adjustment is used to reduce the influence of interference factors on data acquisition.

[0075] In this embodiment, through the collaborative work of the detection interference judgment module, the detection evaluation module and the detection adjustment module, the system not only improves the initial judgment of detection interference, the evaluation and adjustment capabilities of viewing angles and occlusion factors, but also significantly improves the automation, accuracy and resource utilization of performance testing, laying the foundation for an efficient and reliable detection process, and achieving improved accuracy in acquiring performance detection data of the denture engraving machine.

[0076] To summarize, the embodiment of the present application obtains a detection interference index through the acquired detection interference data and determines whether to perform a detection evaluation; if a detection evaluation is performed, a perspective evaluation index is obtained based on the acquired perspective data and the detection interference index to determine whether to perform a perspective adjustment; at the same time, an occlusion evaluation index is obtained based on the acquired occlusion data and the detection interference index to determine whether to perform an occlusion adjustment; finally, whether to perform a detection adjustment is determined based on the perspective evaluation index and the occlusion evaluation index, thereby more accurately and timely adjusting the detection equipment, thereby achieving improved accuracy in acquiring the performance detection data of the denture engraving machine, and effectively solving the problem of inaccurate acquisition of performance detection data of the denture engraving machine in the prior art.

[0077] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product 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.

[0078] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0079] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0080] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0081] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0082] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for processing and analyzing performance detection data of a denture engraving machine, characterized in that: The following steps are involved: A detection interference index is obtained by acquiring detection interference data of the area to be detected, and whether to perform detection evaluation is determined based on the detection interference index. The detection interference index is used to comprehensively quantify the degree to which the detection process is affected by interference; If detection evaluation is performed, a view angle evaluation index is obtained based on the acquired view angle data and the detection interference index to determine whether to perform view angle adjustment. At the same time, an occlusion evaluation index is obtained based on the acquired occlusion data and the detection interference index to determine whether to perform occlusion adjustment. The view angle evaluation index is used to quantify the degree of influence of the view angle on the detection. The occlusion evaluation index is used to quantify the degree of influence of the occlusion on the detection. Determine whether to perform detection adjustment according to the viewing angle evaluation index and the occlusion evaluation index; The detection interference data includes depth gradient, normal vector change rate, normal vector standard deviation, reprojection error and reflectivity; The viewing angle data includes point cloud density, viewing angle coverage and scanning angle; The occlusion data includes grayscale mean, color contrast and missing data point ratio; The specific process of obtaining the detection interference index is as follows: Number the areas to be detected and obtain the detection interference data; Acquire reference detection interference data from a preset database, wherein the reference detection interference data includes a maximum limit value of a depth gradient, a maximum limit value of a normal vector change rate, a maximum limit value of a reprojection error, and a minimum limit value of a reflectivity; A detection interference index is obtained according to the detection interference data and the reference detection interference data, and a detection interference index is obtained based on the detection interference index. The detection interference index includes a depth index, a normal vector index, a reprojection index and a reflection index.

2. The method for processing and analyzing performance detection data for a denture engraving machine according to claim 1, characterized in that: The specific process of determining whether to conduct detection and evaluation is as follows: Compare the detection interference index with the detection threshold obtained from the preset database: If the detection interference index is less than the detection threshold, no detection evaluation is performed; If the detection interference index is not less than the detection threshold, the viewing angle data and the occlusion data are obtained.

3. The method for processing and analyzing performance detection data for a denture engraving machine according to claim 1, characterized in that: The specific process of obtaining the perspective evaluation index is as follows: Obtaining viewing angle data and detecting interference index; Acquire reference viewing angle data from a preset database, wherein the reference viewing angle data includes a minimum limit value of point cloud density, a minimum limit value of viewing angle coverage, and a scanning angle reference interval, wherein the scanning angle reference interval includes a minimum scanning angle value and a maximum scanning angle value; Acquire a viewing angle weight from a preset database, where the viewing angle weight is used to quantify the influence of the detection interference index on the viewing angle evaluation index; Obtaining a scanning angle index based on the scanning angle and the scanning angle reference interval, wherein the scanning angle index is used to quantify the influence of the scanning angle on data acquisition; The point cloud density, viewing angle coverage, minimum limit of point cloud density, minimum limit of viewing angle coverage, scanning angle index, viewing angle weight and detection interference index are processed to obtain the viewing angle evaluation index.

4. The method for processing and analyzing performance detection data for a denture engraving machine according to claim 3, characterized in that: The specific process of determining whether to perform viewing angle adjustment is as follows: Compare the obtained viewing angle evaluation index with the viewing angle threshold obtained from the preset database: If the viewing angle evaluation index is less than the viewing angle threshold, the viewing angle adjustment is not performed; If the viewing angle evaluation index is not less than the viewing angle threshold, automatic path planning is performed and the wide-angle lens is switched. The automatic path planning is used to control the scanning device to automatically adjust the angle and distance around the denture.

5. The performance detection data processing and analysis method for a denture engraving machine according to claim 1, characterized in that: The specific process of obtaining the occlusion evaluation index is as follows: Obtaining occlusion data and detecting interference index and obtaining reference occlusion data from a preset database, wherein the reference occlusion data includes a minimum grayscale mean value and a minimum color contrast value; Obtaining an occlusion weight from a preset database, where the occlusion weight is used to quantify the influence of the detection interference index on the occlusion evaluation index; The occlusion data, the reference occlusion data, the occlusion weight and the detection interference index are processed to obtain an occlusion evaluation index; The specific limiting expression of the occlusion evaluation index is as follows: ； In the formula, n represents the number of the area to be detected, , Indicates the total number of areas to be detected, represents the grayscale mean of the nth area to be detected, Indicates the color contrast of the nth area to be detected, Indicates the proportion of missing data points in the nth region to be detected, represents the minimum grayscale mean value, Indicates the minimum color contrast. represents the occlusion weight, represents the detection interference index of the nth area to be detected, represents the occlusion evaluation index of the nth area to be detected, and e represents a natural constant.

6. The method for processing and analyzing performance detection data for a denture engraving machine according to claim 5, characterized in that: The specific process of determining whether to perform occlusion adjustment is as follows: Compare the obtained occlusion evaluation index with the occlusion threshold obtained from the preset database: If the occlusion evaluation index is less than the occlusion threshold, no occlusion adjustment is performed; If the occlusion evaluation index is not less than the occlusion threshold, light source layout optimization and 3D scanning are performed; The light source layout optimization refers to reducing shadows and dark areas caused by occlusion by adjusting the angle and intensity of the light source.

7. The method for processing and analyzing performance detection data for a denture engraving machine according to claim 1, characterized in that: The specific content of determining whether to perform detection adjustment is as follows: If the viewing angle evaluation index is less than the viewing angle threshold and the occlusion evaluation index is less than the occlusion threshold, a button for executing the detection adjustment measure is sent to the preset staff member, otherwise the detection adjustment is not executed; The detection adjustment includes multi-view scanning and multi-axis control; The multi-view scanning means adding scanning equipment to the area to be inspected to cover various angles of the denture surface.

8. A system using the performance detection data processing and analysis method for a denture engraving machine as claimed in any one of claims 1 to 7, comprising a detection interference judgment module, a detection evaluation module and a detection adjustment module; in, The detection interference judgment module is used to obtain the detection interference index of the detection interference data of the area to be detected, and judge whether to perform detection evaluation based on the detection interference index. The detection interference index is used to comprehensively quantify the degree of influence of interference on the detection process; The detection evaluation module is used to determine whether to perform a viewing angle adjustment based on a viewing angle evaluation index obtained from the viewing angle data and the detection interference index, and to determine whether to perform an occlusion adjustment based on an occlusion evaluation index obtained from the occlusion data and the detection interference index, if a detection evaluation is performed. The viewing angle evaluation index is used to quantify the degree of influence of the viewing angle on the detection, and the occlusion evaluation index is used to quantify the degree of influence of the occlusion on the detection; The detection adjustment module is used to determine whether to perform detection adjustment according to the viewing angle evaluation index and the occlusion evaluation index.

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