Glue path detection method and device based on 3D data, electronic equipment and storage medium

Abstract

The invention relates to the technical field of automatic dispensing, and provides a glue path detection method and device based on 3D data, electronic equipment and a storage medium. The glue path template is determined by utilizing the glue path information of the to-be-detected object, and the glue path area is quickly and accurately positioned based on the 3D data of the to-be-detected object and the glue path template. A Blob tool is used for detecting the glue road area, and a first detection result can be obtained. And when the first detection result is that the glue path is normal, the glue path area is subdivided into a plurality of sub glue path areas, and the subdivision mode can be used for observing and analyzing the condition of the glue path more finely, so that possible local problems can be found. And meanwhile, whether glue overflowing, glue shortage, glue breaking and other problems exist in the glue paths of the sub-glue-path areas or not can be judged by obtaining the glue height and the glue width of each sub-glue-path area.

Description

Technical Field

[0001] This application relates to the technical field of automatic dispensing, and particularly to a dispensing path detection method, device, electronic device and storage medium based on 3D data. Background Art

[0002] Dispensing path detection is a key step to ensure product quality. By accurately measuring the shape, position, etc. of the dispensing path, defects in the dispensing path, such as broken glue, missing glue, overflowing glue, etc., can be detected in a timely manner, thereby preventing defective products from entering the market and ensuring the reliability and stability of the products.

[0003] In the current dispensing path detection process, although 2D black and white cameras can, to a certain extent, identify the shape of the dispensing path and detect defects, their limitations cannot be ignored. Since 2D cameras can only capture the projection of the dispensing path on a two-dimensional plane and cannot accurately obtain the three-dimensional shape of the dispensing path. Therefore, when detecting defects such as broken glue, missing glue, or overflowing glue in the dispensing path, 2D cameras often cannot provide accurate results. Summary of the Invention

[0004] In view of this, this application provides a dispensing path detection method, device, electronic device and storage medium based on 3D data, aiming to solve the problems existing in the prior art and improve the accuracy of dispensing path detection.

[0005] The first aspect of this application provides a dispensing path detection method based on 3D data, and the method includes: Determine a dispensing path template according to the dispensing path information of the object to be measured; Locate the dispensing path area of the object to be measured based on the 3D data of the object to be measured and the dispensing path template; Use the Blob tool to detect based on the dispensing path area to obtain a first detection result; When the first detection result is that the dispensing path is normal, divide the dispensing path area into multiple sub-dispensing path areas; Obtain multiple glue heights and multiple glue widths of each sub-dispensing path area; Detect based on the multiple glue heights and multiple glue widths of each sub-dispensing path area to obtain a second detection result.

[0006] Optionally, the dispensing path template includes a preset standard dispensing path area and a preset standard dispensing path contour, and the using the Blob tool to detect based on the dispensing path area to obtain a first detection result includes: Use the Blob tool to detect based on the dispensing path area to obtain a Blob area; Obtain the number of the Blob areas; Judge whether the number is 1; When the number is 1, calculate the dispensing path area of the Blob area; Determine whether the area of the glue path is equal to the preset standard glue path area; When the area of the glue path is equal to the preset standard glue path area, obtain the glue path contour of the Blob region; Calculate the matching degree between the glue path contour and the preset standard glue path contour; Determine whether the matching degree is greater than the preset matching degree threshold; When the matching degree is greater than the preset matching degree threshold, obtain the first detection result that the glue path is normal.

[0007] Optionally, the obtaining of multiple glue heights and multiple glue widths of each sub-glue path region includes: Intercept cross-sections of each sub-glue path region at preset intervals to obtain multiple cross-sections; Measure the distance between each cross-section in the multiple cross-sections and a reference plane to obtain multiple glue heights; Measure the width of the glue groove corresponding to each cross-section in the multiple cross-sections to obtain multiple glue widths.

[0008] Optionally, the glue path template includes a preset standard glue height and a preset standard glue width, and the performing detection based on the multiple glue heights and multiple glue widths of each sub-glue path region to obtain a second detection result includes: For each sub-glue path region, compare each glue height in the multiple glue heights with the preset standard glue height to obtain a glue height detection result; Compare each glue width in the multiple glue widths with the preset standard glue width to obtain a glue width detection result; Obtain a second detection result according to the glue height detection result and the glue width detection result.

[0009] Optionally, the positioning of the glue path region of the object to be measured based on the 3D data of the object to be measured and the glue path template includes: Obtain the glue path points in the glue path template; Search for the calibration coordinate system corresponding to the glue path information; Obtain the pixel coordinates corresponding to the glue path points in the 3D data; According to the calibration coordinate system, convert the pixel coordinates into mechanical coordinates; Locate the glue path region of the object to be measured according to the mechanical coordinates.

[0010] Optionally, the subdividing of the glue path region into multiple sub-glue path regions includes: Obtain the accuracy requirement of the object to be measured; Subdivide the glue path region into multiple sub-glue path regions according to the accuracy requirement.

[0011] Optionally, the method further includes: Determining a target acquisition device type according to the adhesive material type in the adhesive path information; Collecting 3D data of the object to be measured by using the acquisition device associated with the target acquisition device type.

[0012] A second aspect of the present application provides an adhesive path detection device based on 3D data, the device includes: A template determination module, configured to determine an adhesive path template according to the adhesive path information of the object to be measured; An adhesive path positioning module, configured to position the adhesive path area of the object to be measured based on the 3D data of the object to be measured and the adhesive path template; A first detection module, configured to perform detection based on the adhesive path area by using a Blob tool to obtain a first detection result; A region subdivision module, configured to subdivide the adhesive path area into a plurality of sub-adhesive path areas when the first detection result is that the adhesive path is normal; A height-width acquisition module, configured to acquire a plurality of adhesive heights and a plurality of adhesive widths of each sub-adhesive path area; A second detection module, configured to perform detection based on the plurality of adhesive heights and the plurality of adhesive widths of each sub-adhesive path area to obtain a second detection result.

[0013] A third aspect of the present application provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the computer program, the steps of the adhesive path detection method based on 3D data are implemented.

[0014] A fourth aspect of the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the adhesive path detection method based on 3D data are implemented.

[0015] The glue path detection method, device, electronic device and storage medium based on 3D data provided in the embodiment of the present application use the glue path information of the object to be tested to determine the glue path template. Based on the 3D data of the object to be tested and the positioning of the glue path template, the glue path area can be quickly and accurately captured, providing a reliable basis for subsequent glue path detection. Secondly, the glue path area can be detected using the Blob tool to obtain the first detection result. When the Blob tool is in 3D data, it can efficiently identify and analyze the characteristics of the target area, so it can quickly and accurately determine whether the glue path is normal, thereby improving the efficiency and quality of glue path detection. When the first detection result is that the glue path is normal, the glue path area is further subdivided into multiple sub-glue path areas. The subdivision method can observe and analyze the glue path status more finely, which helps to find possible local problems. At the same time, by obtaining the glue height and glue width of each sub-glue path area, it can be determined whether the glue path in the sub-glue path area has problems such as overflow, lack of glue, and broken glue, thereby providing strong support for quality control in the production process, which is the recognition and defect detection of glue path shape based on 2D black and white cameras and color cameras in the prior art that cannot be determined. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a flow chart of a glue path detection method based on 3D data shown in an embodiment of the present application; Figure 2 Schematic diagram of the application environment of the 3D sensor used in the embodiment of the present application for glue path detection; Figure 3 is a schematic diagram of the glue path area shown in the embodiment of the present application; Figure 4 is a schematic diagram of a cross-section of a glue path area shown in an embodiment of the present application; Figure 5 is a schematic diagram of the glue height and glue width of the sub-glue path area shown in the embodiment of the present application; Figure 6 It is a functional module diagram of a glue road detection device based on 3D data shown in an embodiment of the present application; Figure 7 It is a structural diagram of an electronic device shown in an embodiment of the present application. DETAILED DESCRIPTION

[0017] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations comprising one or more of the listed items.

[0018] Hereinafter, the terms "first" and "second" are only for descriptive purposes and should not be construed as implying or suggesting relative importance or implicitly indicating the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the embodiments of the present application, unless otherwise specified, the meaning of "a plurality" is two or more.

[0019] Figure 1 is a flowchart of a glue path detection method based on 3D data provided by an embodiment of the present application. The glue path detection method based on 3D data specifically includes the following steps.

[0020] S11. Determine a glue path template according to the glue path information of the object to be measured.

[0021] Herein, the object to be measured refers to an object or component after dispensing by a dispensing system. The object to be measured has completed the dispensing process during production, and now subsequent glue path detection or evaluation is required.

[0022] The glue path information of the object to be measured may include the product type and the glue material type. The product type refers to the type or model of the object to be measured, and the glue material type refers to the type of the material of the glue path on the object to be measured. The glue material type in the embodiments of the present application may include two major types, one is fully transparent, and the other is non-fully transparent (opaque and semi-transparent).

[0023] To achieve the accuracy and efficiency of automatic glue path detection, before detecting the glue path of the object to be measured, the product type and the glue material type of the object to be measured can be obtained first through machine vision, barcode collection, radio frequency identification (RFID) or manual input, etc., so as to obtain the glue path information of the object to be measured. After the control device (for example, the host computer) obtains the glue path information of the object to be measured, it searches and switches to the glue path template corresponding to the glue path information from the pre-stored glue path information template table. The glue path information template table records the mapping relationship between different glue path information and glue path templates, and different glue path information corresponds to different glue path templates. The glue path template is obtained by analyzing a qualified sample and collecting relevant information of the qualified sample, and may include but is not limited to: glue path feature points, glue path standard contour, glue path standard area corresponding to the glue path standard contour, glue path standard height, glue path standard width, etc.

[0024] S12. Locate the glue path area of the object to be measured based on the 3D data of the object to be measured and the glue path template.

[0025] After determining the glue path template, a 3D sensor (for example, a 3D camera) can be activated, as Figure 2As shown, the object to be measured is scanned. Since the 3D sensor is set on the robotic arm, the movement of the robotic arm can drive the movement of the 3D sensor. During the movement of the 3D sensor, the 3D sensor collects the 3D data of the object to be measured. The 3D data includes the planar coordinate information (X, Y) and height information (Z) of the object to be measured, reflecting the spatial structure information of the object to be measured.

[0026] The 3D data of the object to be measured is compared with the glue path template to locate the glue path area of the object to be measured in the 3D data, as Figure 3 shown, so as to detect the glue path in the glue path area.

[0027] In an alternative embodiment, in order to ensure that the 3D data of the object to be measured collected is more accurate and improve the accuracy of subsequent glue path detection, the embodiments of the present application may determine the target acquisition device type according to the glue material type in the glue path information, and use the acquisition device associated with the target acquisition device type to collect the 3D data of the object to be measured.

[0028] Different glue material types (such as fully transparent, opaque, semi-transparent) have differences in optical properties. Using the same acquisition device to collect 3D data will directly affect the capture and analysis of the object surface information by the acquisition device. Using an acquisition device matching the glue material type can more accurately obtain the 3D data of the object to be measured and reduce the error caused by device mismatch. Moreover, by flexibly selecting the acquisition device according to the glue path information, the requirements of different application scenarios can be adapted, making the entire detection system more flexible and versatile.

[0029] A matching table or database of glue material type and acquisition device type is pre-stored in the control device. After identifying the glue material type of the object to be measured, the acquisition device type corresponding to the glue material type of the object to be measured can be queried from the matching table or database, so as to determine the target acquisition device. For example, for opaque or semi-transparent glue materials, structured light or time-of-flight cameras can be selected as the target acquisition device to obtain the 3D data of the object to be measured. For fully transparent glue materials, line spectrum sensors or other spectrum sensors suitable for transparent substances can be selected as the target acquisition device to obtain the 3D data of the object to be measured.

[0030] In an alternative embodiment, in order to further ensure that the 3D data of the object to be measured collected is more accurate and improve the accuracy of subsequent glue path detection, the embodiments of the present application may also, after determining the target acquisition device, determine the acquisition parameters of the target acquisition device according to the product type in the glue path information, so that the target acquisition device collects the 3D data of the object to be measured according to the acquisition parameters. The acquisition parameters are parameters used to control the working state of the target acquisition device, and may include acquisition field of view, exposure rate, exposure time, focal length, aperture size, acquisition speed, acquisition spacing, etc.

[0031] In the above optional embodiment, based on the mapping relationship between the product type and the acquisition parameters, the acquisition parameters are adjusted to adapt to the image acquisition requirements of different products, improving the accuracy and adaptability of 3D data acquisition, thereby providing a more reliable basis for subsequent glue path detection and improving the accuracy of dispensing. At the same time, this method can also reduce the operation difficulty and cost, improve the automation level and efficiency of the production line, and is applicable to glue path detection with a high degree of automation, especially when there are many types of objects to be measured and large differences in shape and size.

[0032] In an optional embodiment, the positioning of the glue path area of the object to be measured based on the 3D data of the object to be measured and the glue path template includes: Obtain the glue path points in the glue path template; Search for the calibration coordinate system corresponding to the glue path information; Obtain the pixel coordinates corresponding to the glue path points in the 3D data; According to the calibration coordinate system, convert the pixel coordinates into mechanical coordinates; Locate the glue path area of the object to be measured according to the mechanical coordinates.

[0033] The glue path points refer to the preset feature points in the glue path template, which are used to accurately locate the glue path area on the object to be measured. The glue path points are usually obtained through analysis of qualified samples and are representative, such as corner points, edge points, or points with large curvature changes. The shape of the feature points can be circular, triangular, trapezoidal, etc., and the embodiments of the present application do not limit this.

[0034] For products of the same type, the positions of the feature points on the product itself are fixed. By identifying the feature points, the position of the object to be measured can be accurately identified, thereby accurately positioning the glue path points and the glue path area. For products of the same type, the glue path points and the glue path area are the same. For products of different types, the glue path points and the glue path area may be the same or different.

[0035] It should be understood that before dispensing the object to be tested, it is usually necessary to perform hand-eye calibration on the dispensing equipment, that is, to calibrate the 3D sensor and the robotic arm, and establish a mapping relationship between the pixel coordinate system and the mechanical coordinate system to achieve precise positioning. The coordinate system corresponding to the 3D sensor is the pixel coordinate system, and the pixel coordinate refers to the position information of each pixel in the image. For 3D data, the pixel coordinates usually contain three dimensions (X1, Y1, Z1), which correspond to the pixel width, pixel length and pixel height information in the 3D data respectively. The coordinate system corresponding to the robotic arm is the mechanical coordinate system. The mechanical coordinates are the coordinate system used in the robotic arm or dispensing equipment to describe the position of the tool or dispensing head in the mechanical space. After converting the pixel coordinates to the mechanical coordinates, the mechanical coordinates also contain three dimensions (X2, Y2, Z2), which correspond to the physical width, physical length and physical height information in the mechanical space respectively.

[0036] In the process of automated dispensing and glue path detection, the width direction of the glue groove of the object to be tested is the X-axis direction, the extension direction of the glue groove is the Y-axis direction, and the direction perpendicular to the bottom surface of the glue groove is the Z-axis direction. Since the dispensing operation usually needs to be carried out within the length of the glue groove, the glue path point is located on the length of the glue groove, that is, in the Y-axis direction. After determining the product type of the object to be tested, the calibration coordinate system corresponding to the product type in the glue path information is searched from the preset database or configuration file. Through the calibration coordinate system, the pixel coordinates of the feature points can be mapped to mechanical coordinates. The calibration coordinate system is determined in advance through a calibration process, and is used to convert the pixel coordinates in the image data into mechanical coordinates in the actual motion space of the robot arm or dispensing equipment. For the same type of product, the calibration coordinate system is unique, and this unique calibration coordinate system is equivalent to the reference of the X and Y axes. The mapping process is a prior art, and this application will not be elaborated in detail here.

[0037] The control device pre-stores the correspondence between the characteristic points of different types of products and the glue path points, glue path areas, and glue dispensing trajectories. After identifying the characteristic points in the 3D data and obtaining the mechanical coordinates of the characteristic points, the glue path points and glue path areas corresponding to the species to be tested and the characteristic points are located according to the correspondence between the characteristic points of different types of products and the glue path points, glue path areas, and glue dispensing trajectories.

[0038] In the above optional implementation, the pixel coordinates of the glue path points are extracted from the 3D data, and the pixel coordinates of the glue path points are converted into mechanical coordinates through the mapping relationship of the calibrated coordinate system. The glue path area is then located through the mechanical coordinates of the glue path points. This can adapt to the glue path detection requirements of different product types, reduce human errors and dependence, and improve the consistency and repeatability of glue path detection.

[0039] S13. Use the Blob tool to detect based on the glue path area to obtain a first detection result.

[0040] Since the placement position of the product may not be fixed, directly locating the glue path may be affected by the position change, resulting in inaccurate positioning. By anchoring to obtain the feature points in the 3D data, the X and Y coordinate information of the feature points and the Z angle can be obtained, so as to initially determine the possible position of the glue path. The glue path area obtained through the glue path template may contain some noises, irrelevant details or parts with blurred edges. The Blob tool can identify and separate the connected areas with similar attributes based on the gray level, color or other attributes of the image. Therefore, through the Blob tool, the part that most matches the glue path characteristics can be further screened out from the obtained glue path area, so as to obtain a more accurate glue path positioning result.

[0041] The glue path area is the part in the 3D data that represents the glue path and is the target area for the Blob tool to detect. The Blob tool is used to identify the connected areas (Blobs) with similar attributes in the image. Using the Blob tool to detect based on the glue path area, the obtained detection result is the first detection result. The first detection result can initially determine whether there are defects in the glue path of the object to be tested.

[0042] In an optional embodiment, the using the Blob tool to detect based on the glue path area to obtain a first detection result includes: Use the Blob tool to detect based on the glue path area to obtain a Blob area; Obtain the number of the Blob areas; Judge whether the number is 1; When the number is 1, calculate the glue path area of the Blob area; Judge whether the glue path area is equal to the preset standard glue path area; When the glue path area is equal to the preset standard glue path area, obtain the glue path contour of the Blob area; Calculate the matching degree between the glue path contour and the preset standard glue path contour; Judge whether the matching degree is greater than the preset matching degree threshold; When the matching degree is greater than the preset matching degree threshold, obtain the first detection result that the glue path is normal.

[0043] Apply the Blob tool to the glue path area. By setting appropriate thresholds and parameters, the connected areas with similar attributes, that is, the Blob areas, can be identified. The Blob area is an area formed by adjacent connection of the same pixel or similar pixels (similar gray levels). The Blob tool can simplify the three-dimensional space image of the glue path.

[0044] Under normal circumstances, the glue path should be a whole, and the number of Blob regions filtered by the Blob tool should be 1. If the number of Blob regions filtered by the Blob tool is 1, it indicates that the glue path is complete and there is no broken glue. If the number of Blob regions filtered by the Blob tool is not 1, it indicates that there may be scattered glue, broken glue or foreign object interference in the glue path. If there is no broken glue, the number of Blob regions must be 1. Therefore, it is possible to judge whether there is a broken glue problem in the glue path according to whether the number of Blob regions is 1.

[0045] When the number of Blob regions is 1, the pixel area of the Blob region is calculated by the Blob tool to obtain the glue path area of the Blob region. Then, the glue path area is compared with the preset standard glue path area to preliminarily judge whether there is a problem of excessive or insufficient glue. The preset standard glue path area is the standard value of the glue path area set according to product specifications or process requirements. If the glue path area is greater than the preset standard glue path area, it indicates that the glue volume is too much, which means that the application or spraying of glue is excessive during the production process. Excessive glue may cause problems such as glue overflow and glue drops during the use of the product, affecting the quality and stability of the product. If the glue path area is less than the preset standard glue path area, it indicates that the glue volume is insufficient, which means that there are problems such as uneven application and partial area missed coating during the glue application process. Insufficient glue volume may cause problems such as loose connection and poor sealing of the product, affecting the normal use of the product.

[0046] When the glue path area is equal to the preset standard glue path area, the edge information of the Blob region is identified and extracted by the Blob tool to obtain the glue path contour of the Blob region. The glue path contour of the Blob region is compared with the preset standard glue path contour, and the matching degree between the glue path contour and the preset standard glue path contour is calculated to judge whether the shape and integrity of the glue path meet the requirements. If the matching degree between the glue path contour of the Blob region and the preset standard glue path contour is greater than the preset matching degree threshold, it indicates that the actually detected glue path shape is very close to the standard shape, that is, the glue path is well formed and meets the process requirements. On the contrary, if the matching degree between the glue path contour of the Blob region and the preset standard glue path contour is less than the preset matching degree threshold, it indicates that there is a large difference between the actually detected glue path shape and the standard shape, and there may be problems such as uneven glue application and glue path deformation. The preset standard glue path contour is the standard glue path contour set according to product specifications or process requirements. The calculation of the matching degree is usually based on similarity measurement methods between contours, such as the distance between contours, shape context, etc.

[0047] When the number of Blob regions is 1, the glue path area of the Blob region is equal to the preset standard glue path area, and the matching degree between the glue path contour of the Blob region and the preset standard glue path contour is greater than the preset matching degree threshold, a first detection result of normal glue path is obtained. When the number of Blob regions is not 1, or when the number of Blob regions is 1, but the glue path area of the Blob region is greater than or less than the preset standard glue path area, or when the number of Blob regions is 1 and the glue path area of the Blob region is equal to the preset standard glue path area, but the matching degree between the glue path contour of the Blob region and the preset standard glue path contour is less than the preset matching degree threshold, a first detection result of abnormal glue path is obtained.

[0048] In an alternative embodiment, after obtaining the first detection result of abnormal glue path, the object to be measured can be scanned again, the glue path region can be located based on the 3D data obtained from the re-scanning, and the Blob tool can be used to detect based on the re-located glue path region.

[0049] In the above alternative embodiment, by first judging whether the number of Blob regions is 1, abnormal situations such as glue path breakage, bifurcation or the presence of other interfering objects can be quickly excluded, ensuring the accuracy of subsequent analysis; after confirming that the number of Blob regions is 1, further judging whether the glue path area is equal to the preset standard glue path area is to ensure that the glue amount meets the process requirements. If the glue path area does not meet the preset standard glue path area, even if the glue path contour is perfect, the normal use of the product cannot be guaranteed; after the glue path area meets the preset standard glue path area, the last step is to judge whether the matching degree between the glue path contour and the preset standard glue path contour is higher than the preset matching degree threshold, which is to ensure that the shape and integrity of the glue path meet the standards. Even if the glue path area is correct, but if the shape is irregular or there are defects, it will also affect the quality and performance of the product. That is, the above alternative embodiment comprehensively judges the normality of the glue path from multiple dimensions including the number, area and contour of the Blob region, improving the reliability and accuracy of glue path detection.

[0050] In addition, using the Blob tool for automated detection reduces manual intervention and errors, and improves the detection efficiency.

[0051] S14. When the first detection result is that the glue path is normal, the glue path region is subdivided into multiple sub-glue path regions.

[0052] When it is determined that the glue path is normal, a mask corresponding to the Blob region can be generated, and this mask is applied to the glue path region, only the glue path region marked as 1 in the mask is retained, and other regions are set to 0 or transparent, so as to achieve the purpose of separating the glue path region from the 3D data.

[0053] Region subdivision is to further divide the extracted glue path region into multiple smaller sub-regions. Dividing the glue path region into multiple sub-glue path regions is for more refined analysis and processing of the glue path. Due to the diversity of the glue path contour and the impact of speed changes during the dispensing process on the glue volume, treating the entire glue path region as a whole may not capture all the details and changes. Through region subdivision, the glue path can be divided into multiple smaller parts, and each part can be analyzed and evaluated separately, so as to more accurately locate and identify anomalies within each region, such as insufficient glue volume, glue path breakage, etc.

[0054] In an optional implementation manner, the dividing the glue path region into multiple sub-glue path regions includes: Obtain the precision requirement of the object to be measured; According to the precision requirement, divide the glue path region into multiple sub-glue path regions.

[0055] Among them, the precision requirement refers to the precise degree requirement for the dispensing operation of the object to be measured. The level of the precision requirement directly affects the number of sub-regions divided.

[0056] The precision requirement can be obtained by reading the design document, process requirement of the object to be measured or communicating with the user. The precision requirement can be a specific value, such as the allowable deviation range of the dispensing position, the fluctuation range of the glue volume, etc. The precision requirement can also be in the form of grades, such as first grade, second grade, third grade, etc.

[0057] The precision requirement of the object to be measured can be compared with multiple precision requirement ranges to determine the target precision requirement range where the precision requirement of the object to be measured is located, obtain the target number of divisions corresponding to the target precision requirement range, and evenly divide the glue path region into multiple sub-regions according to the target number of divisions. The length of each sub-region is the same, that is, each sub-region is the same in the Y-axis direction.

[0058] In the above optional implementation manner, different precision requirements are adapted by reasonably dividing the sub-glue path regions. The division of the sub-glue path regions can be dynamically adjusted according to the level of the precision requirement to more accurately capture the minute changes or anomalies in the glue path. For products with higher precision requirements, more sub-glue path regions can be divided to more accurately identify and locate problems in the glue path; for products with lower precision requirements, the division of sub-regions can be appropriately reduced to simplify the detection process and improve the processing speed.

[0059] S15, obtain multiple glue heights and multiple glue widths of each sub-glue path region.

[0060] After dividing the glue path area into multiple sub - areas, edge detection, binarization, etc. can be performed on the images corresponding to each sub - area to identify the boundaries of the sub - areas. By calculating the coordinate positions of the boundary pixel points, the width, length, and height of the sub - area can be determined, and the glue width, glue length, and glue height of the sub - area can be correspondingly obtained. The glue width, glue length, and glue height of the sub - area respectively refer to the dimensions of the sub - area in the X, Y, and Z axis directions.

[0061] In an alternative embodiment, the obtaining of multiple glue heights and multiple glue widths of each sub - glue path area includes: Intercepting cross - sections of each sub - glue path area at preset intervals to obtain multiple cross - sections; Measuring the distances between each of the multiple cross - sections and a reference plane to obtain multiple glue heights; Measuring the widths of the glue grooves corresponding to each of the multiple cross - sections to obtain multiple glue widths.

[0062] Determine a suitable distance or time interval, and then cut each sub - glue path area at this distance or time interval to obtain multiple cross - sections. That is, a cross - section refers to a plane obtained by cutting the sub - glue path area perpendicular to the glue path direction, as Figure 4 shown, for observing and measuring the glue height and glue width.

[0063] Refer to Figure 5 shown. In the three - dimensional spatial state image model of the sub - glue path area, two clear contour lines are formed on both sides of the glue path. Taking the intersection points of each cross - section and the contour lines as sampling points, at each sampling point, a measuring tool (such as a microscope, a laser rangefinder, etc.) can be used to obtain the glue path edges on both sides in the normal direction of the glue path to measure the glue width. At the same time, at each sampling point, use the measuring tool to measure the distance between each cross - section and the reference plane to obtain the glue height. The glue width reflects the dimension of the colloid in the horizontal direction, that is, the width of the colloid in the glue groove, and the glue height reflects the thickness of the colloid in the vertical direction, that is, the distance from the top of the colloid to the reference plane.

[0064] In the above - mentioned alternative embodiment, by intercepting cross - sections of the sub - glue path area and measuring the glue heights and glue widths of multiple cross - sections, the glue height and glue width distribution of the sub - glue path area can be more accurately reflected, the differences and change trends of the glue layer at different positions can be analyzed, and the errors caused by single measurement can be avoided.

[0065] In an alternative embodiment, a parallel processing method can be adopted to simultaneously obtain the glue heights and glue widths of multiple sub - glue path areas to speed up the processing speed and improve the processing efficiency.

[0066] In an alternative embodiment, the glue width, glue length, and glue height of each sub-region can be recorded and organized in the form of a table or database, facilitating subsequent dispensing control and data analysis for products of the same type. A unique identifier can also be assigned to each sub-region for identification and positioning during the dispensing process.

[0067] S16, perform detection based on multiple glue heights and multiple glue widths of each sub-glue path region to obtain a second detection result.

[0068] For each sub-glue path region, the detection result obtained through analysis and processing based on the measurement data of multiple glue heights and glue widths of the sub-glue path region is the second detection result. The second detection result is used to determine whether the quality or performance of the sub-glue path region meets the preset standards or requirements.

[0069] In an alternative embodiment, the performing detection based on multiple glue heights and multiple glue widths of each sub-glue path region to obtain a second detection result includes: For each sub-glue path region, compare each glue height among the multiple glue heights with the preset standard glue height to obtain a glue height detection result; Compare each glue width among the multiple glue widths with the preset standard glue width to obtain a glue width detection result; Obtain the second detection result according to the glue height detection result and the glue width detection result.

[0070] The preset standard glue height is a reference value of the glue height set according to product requirements or process standards, and the preset standard glue width is a reference value of the glue width set according to product requirements or process standards. Compare each glue height among the multiple glue heights with the preset standard glue height, and compare each glue width among the multiple glue widths with the preset standard glue width to evaluate whether the height and width of the colloid in actual production meet the preset quality standards or process requirements.

[0071] For each sub - glue path area, each of its multiple glue heights is compared with a preset standard glue height to evaluate whether the height of the glue body in actual production meets the preset quality standards or process requirements. By comparing the actually measured glue height with the preset standard glue height, a detection result regarding the quality of the glue body height can be obtained. The glue height detection result describes the deviation between the actual glue height and the preset standard glue height. If the actual glue height is higher than the preset standard glue height, it indicates that an excess may have occurred during the coating or filling process of the glue body, resulting in the glue height exceeding the normal range and causing a problem of glue hanging. A too - high glue height may affect the performance of the product. For example, it may increase the thickness of the product, causing the size to not meet the requirements, or in some applications, it may affect the usage effect of the product. If the actual glue height is lower than the preset standard glue height, it indicates that a deficiency may have occurred during the coating or filling process of the glue body, resulting in the glue height not reaching the expected standard, causing problems such as lack of glue or broken glue. A too - small glue height may affect the performance and appearance of the product. For example, it may cause problems such as insufficient product strength, poor sealing, or uneven appearance.

[0072] For each sub - glue path area, each of its multiple glue widths is compared with a preset standard glue width. By comparing the actually measured glue width with the preset standard glue width, a detection result regarding the quality of the glue body width can be obtained. The glue width detection result describes the deviation between the actual glue width and the preset standard glue width. When the actual glue width is greater than the preset standard glue width, it indicates that an excess has occurred during the coating or filling process of the glue body, resulting in the glue width exceeding the normal range, which may mean a problem of glue overflow. If the glue width is less than the preset standard glue width, it indicates that a deficiency may have occurred during the coating or filling process of the glue body, resulting in the glue width not reaching the expected standard, which may indicate a lack of glue. A too - small glue width may affect the performance or appearance of the product. For example, it may cause problems such as poor product sealing, insufficient strength, or poor appearance.

[0073] In an optional implementation manner, to further improve the accuracy and reliability of the glue path quality detection for the sub - glue path area, the present application can also preset a first quantity threshold and a second quantity threshold to more precisely determine whether problems such as glue hanging, lack of glue, broken glue, or glue overflow occur in the sub - glue path area.

[0074] For glue height detection, a first quantity threshold is preset. For each sub-glue path region, count the number of profiles (the first quantity) with glue height higher than the preset standard glue height and the number of profiles (the second quantity) with glue height lower than the preset standard glue height within this region. Compare the first quantity and the second quantity with the first quantity threshold respectively. If the first quantity (i.e., the number of profiles with glue height higher than the standard) is greater than the first quantity threshold, that is, the glue height of most profiles exceeds the standard, then it is determined that the glue height detection result corresponding to this sub-glue path region has a problem of glue sticking. If the second quantity (i.e., the number of profiles with glue height lower than the standard) is greater than the first quantity threshold, that is, the glue height of most profiles does not reach the standard, then it is determined that the glue height detection result corresponding to this sub-glue path region has a problem of glue shortage or glue breakage. If both the first quantity and the second quantity are lower than the first quantity threshold, that is, the first quantity (i.e., the number of profiles with too high glue height) and the second quantity (i.e., the number of profiles with too low glue height) are both below the first quantity threshold, which means the change in glue height is within an acceptable range and there are not too many profiles showing abnormally high or low glue height, then it is determined that the glue height detection result corresponding to this sub-glue path region is normal glue height.

[0075] For glue width detection, a second quantity threshold is preset. For each sub-glue path region, count the number of profiles (the third quantity) with glue width higher than the preset standard glue width and the number of profiles (the fourth quantity) with glue width lower than the preset standard glue width within this region. Compare the third quantity and the fourth quantity with the second quantity threshold respectively. If the third quantity (i.e., the number of profiles with glue width higher than the standard) is greater than the second quantity threshold, that is, the glue width of most profiles exceeds the standard, then it is determined that the glue width detection result corresponding to this sub-glue path region has a problem of glue overflow. If the fourth quantity (i.e., the number of profiles with glue width lower than the standard) is greater than the second quantity threshold, that is, the glue width of most profiles does not reach the standard, then it is determined that the glue width detection result corresponding to this sub-glue path region has a problem of glue shortage. If both the third quantity and the fourth quantity are lower than the second quantity threshold, that is, the third quantity (i.e., the number of profiles with too wide glue width) and the fourth quantity (i.e., the number of profiles with too narrow glue width) are both below the second quantity threshold, which means the change in glue width is within an acceptable range and there are not too many profiles showing abnormally wide or narrow glue width, then it is determined that the glue width detection result corresponding to this sub-glue path region is normal glue width.

[0076] By means of the concept of the number of profiles and the quantity threshold, and combined with the number of profiles with glue height and / or glue width exceeding or being lower than the standard obtained from actual measurement, a quantitative index is provided to evaluate the degree of problems existing in the glue path. This quantitative method is more accurate than simply comparing the glue height and / or glue width, and can better reflect the actual situation of the glue path quality.

[0077] Secondly, the number of contours can also help identify the distribution of glue coating / glue overflow / glue shortage. If the glue coating / glue overflow / glue shortage is mainly concentrated on a few contours, it may be just a local problem and can be solved by adjusting the glue coating process or equipment parameters. If the glue coating / glue overflow / glue shortage occurs on multiple contours, it may mean that there is a problem with the entire glue coating system and more in-depth inspection and adjustment are needed.

[0078] In addition, by comparing the glue coating / glue overflow / glue shortage situations of different contours, the reasons for the glue coating / glue overflow / glue shortage can also be analyzed. For example, if the glue coating / glue overflow / glue shortage mainly occurs at the start or end of the glue coating process, it may be due to inaccurate start and stop control of the glue coating head. If the glue coating / glue overflow / glue shortage is distributed throughout the glue coating process, it may be due to improper setting of parameters such as glue coating speed, pressure, or temperature.

[0079] After obtaining the glue height detection result and glue width detection result of the sub-glue path area, a comprehensive judgment is made based on the glue height detection result and glue width detection result to obtain the second detection result. The second detection result is an evaluation of the overall quality status of the sub-glue path area in terms of both glue height and glue width. The second detection result includes: Normal: If both the glue height detection result and the glue width detection result show normal (i.e., not exceeding the preset standard range), the second detection result is that the glue path is normal. That is, both the glue height and the glue width meet the requirements.

[0080] Severe glue overflow: If the glue height detection result shows a glue coating problem (excessive glue height) and the glue width detection result also shows a glue overflow problem (excessive glue width), it can be judged that there is a severe glue overflow phenomenon in the sub-glue path area. That is, both the glue height and the glue width exceed the standard.

[0081] Severe glue shortage: If the glue height detection result shows a glue shortage or broken glue problem (insufficient glue height) and the glue width detection result also shows a glue shortage problem (insufficient glue width), it can be judged that there is a severe glue shortage phenomenon in the sub-glue path area. That is, both the glue height and the glue width are below the standard.

[0082] Mixed problem: If the glue height detection result and the glue width detection result are inconsistent (for example, the glue height is excessive while the glue width is normal, or the glue height is insufficient while the glue width is excessive), it indicates that there are different types of quality problems with the glue height and glue width in the sub-glue path area, such as local abnormalities during the coating process or instability of the equipment state.

[0083] In the above optional implementation, by comparing the actual measured value with the preset standard value, accurate detection of the quality of the glue path area is achieved, reducing the possibility of misjudgment and missed judgment, promptly discovering and handling unqualified glue path areas, which helps to ensure the consistency and stability of the overall product quality. At the same time, the automated measurement and judgment process reduces the complexity and time consumption of manual operations and improves production efficiency.

[0084] In the glue path quality inspection process of the glue path area, the quality result of the glue path area (such as OK or NG, that is, qualified or unqualified) is fed back to the MES (Manufacturing Execution System). By promptly feeding back the glue path quality result, the MES system can grasp the quality status of the production line in real time, and thus make corresponding adjustments and decisions. Specifically, when the glue path quality inspection is completed, the system will automatically upload the quality result (OK or NG) and related inspection data to the MES system. The MES system processes and analyzes these data to generate corresponding quality reports and statistical information. If it is found that the glue path quality of a certain batch of materials is generally unqualified, the management can immediately suspend the production of this batch, investigate the reasons and make adjustments to prevent unqualified products from continuing to flow into the next process. The MES system can also optimize the production process according to the quality results. By analyzing the quality data and production data, the system can identify the key factors affecting the glue path quality, such as equipment status, process parameters, etc., and put forward corresponding improvement suggestions. These suggestions can help the production department improve product quality and production efficiency and reduce production costs.

[0085] The glue path detection method based on 3D data provided by the embodiments of this application solves the problems existing in the prior art and improves the accuracy and efficiency of glue path detection. First, a glue path template is determined using the glue path information of the object to be measured. Based on the 3D data of the object to be measured and the positioning of the glue path template, rapid and accurate capture of the glue path area can be achieved, providing a reliable basis for subsequent glue path detection. Second, the Blob tool is used to detect the glue path area, and a first detection result can be obtained. When dealing with 3D data, the Blob tool can efficiently identify and analyze the characteristics of the target area, so it can quickly and accurately judge whether the glue path is normal, improving the efficiency and quality of glue path detection. When the first detection result indicates that the glue path is normal, the glue path area is further divided into multiple sub-glue path areas. This subdivision method can more finely observe and analyze the condition of the glue path, helping to discover possible local problems. At the same time, by obtaining the glue height and glue width of each sub-glue path area, it is possible to judge whether there are problems such as glue overflow, glue shortage, and glue breakage in the glue path of the sub-glue path area, thus providing strong support for quality control in the production process, which cannot be judged by the prior art based on 2D black-and-white cameras and color cameras for glue path shape recognition and defect detection.

[0086] In addition, in the prior art, black and white cameras are mainly used to detect the shape of the glue path that is matte, opaque, and has an obvious threshold difference from the background, while color cameras identify the shape of the glue path and detect defects by using image processing technology to obtain images of the glue path that is transparent or has a small threshold difference from the product surface. These methods have limitations in detecting shiny or semi-transparent glue paths. This application also determines the type of target acquisition device according to the type of glue material in the glue path information, and uses the acquisition device associated with the type of target acquisition device to collect 3D data of the object to be measured. For opaque or semi-transparent types of glue materials, a 3D camera can be selected to obtain 3D data of the object to be measured. For fully transparent types of glue materials, a line spectral sensor can be selected to obtain 3D data of the object to be measured, expanding the scope of application, enabling the glue path detection method provided by the embodiments of this application to be flexibly applied in the detection of multiple types of products, without considering the focal length problem and the inconsistency of glue color, and improving the detection efficiency and reliability.

[0087] The application scenarios of this application include, but are not limited to, automated glue application production lines for products such as mobile phones, tablets, and monitors.

[0088] Figure 6 It is a structural diagram of a glue path detection device based on 3D data provided by an embodiment of this application.

[0089] In some embodiments, the glue path detection device 6 based on 3D data may include multiple functional modules composed of computer program segments. The computer programs of each program segment in the glue path detection device 6 based on 3D data can be stored in the memory of the electronic device and executed by at least one processor to perform the functions of glue path detection based on 3D data (see details in Figure 1 the description).

[0090] In this embodiment, the glue path detection device 6 based on 3D data can be divided into multiple functional modules according to the functions it performs. The functional modules may include: a template determination module 601, a data acquisition module 602, a glue path positioning module 603, a first detection module 604, a region subdivision module 605, a height and width acquisition module 606, and a second detection module 607. The module referred to in this application means a series of computer program segments that can be executed by at least one processor and can complete fixed functions, and are stored in the memory. In this embodiment, the functions of each module will be described in detail in subsequent embodiments.

[0091] The template determination module 601 is used to determine a glue path template according to the glue path information of the object to be measured.

[0092] The data acquisition module 602 is configured to determine a target acquisition device type according to the type of adhesive material in the adhesive path information of the object to be measured, and use the acquisition device associated with the target acquisition device type to acquire 3D data of the object to be measured.

[0093] The adhesive path positioning module 603 is configured to locate the adhesive path area of the object to be measured based on the 3D data of the object to be measured and the adhesive path template.

[0094] The first detection module 604 is configured to use the Blob tool to perform detection based on the adhesive path area to obtain a first detection result.

[0095] The area subdivision module 605 is configured to subdivide the adhesive path area into multiple sub-adhesive path areas when the first detection result indicates that the adhesive path is normal.

[0096] The height and width acquisition module 606 is configured to acquire multiple adhesive heights and multiple adhesive widths of each sub-adhesive path area; The second detection module 607 is configured to perform detection based on the multiple adhesive heights and multiple adhesive widths of each sub-adhesive path area to obtain a second detection result.

[0097] It should be understood that the various change modes and specific embodiments in the adhesive path detection method based on 3D data provided in the above embodiments are equally applicable to the adhesive path detection device based on 3D data in this embodiment. Through the detailed description of the adhesive path detection method based on 3D data, those skilled in the art can clearly know the implementation process of the adhesive path detection device based on 3D data in this embodiment. For the sake of brevity of the specification, it will not be elaborated here.

[0098] An embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, all or part of the steps of the adhesive path detection method based on 3D data are implemented.

[0099] Refer to Figure 7 As shown, it is a schematic structural diagram of an electronic device provided by an embodiment of the present application. In a preferred embodiment of the present application, the electronic device 7 includes a memory 71, at least one processor 72, and at least one communication bus 73.

[0100] Those skilled in the art should understand that Figure 7 the structure of the electronic device shown does not constitute a limitation to the embodiments of the present application. It can be a bus structure or a star structure. The electronic device 7 may further include more or fewer other hardware or software than shown, or different component arrangements.

[0101] In some embodiments, the electronic device 7 is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions. Its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits, programmable gate arrays, digital signal processors, and embedded devices, etc. The electronic device 7 may further include a client device, and the client device includes, but is not limited to, any electronic product that can perform human-computer interaction with the client through means such as a keyboard, mouse, remote control, touchpad, or voice control device. For example, personal computers, tablet computers, smart phones, digital cameras, etc.

[0102] It should be noted that the electronic device 7 is only an example. Other existing or future electronic products that can be adapted to this application should also be included within the protection scope of this application and are hereby incorporated by reference.

[0103] In some embodiments, a computer program is stored in the memory 71, and when the computer program is executed by the at least one processor 72, all or part of the steps in the method for detecting glue paths based on 3D data as described are implemented. The memory 71 includes a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electrically-erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM), or other optical disc memories, magnetic disk memories, tape memories, or any other computer-readable medium that can be used to carry or store data. Further, the computer-readable storage medium may mainly include a program storage area and a data storage area. Among them, the program storage area may store an operating system, application programs required for at least one function, etc.

[0104] In some embodiments, the at least one processor 72 is the control core (Control Unit) of the electronic device 7, connecting various components of the entire electronic device 7 through various interfaces and lines. By running or executing programs or modules stored in the memory 71, and by invoking data stored in the memory 71, it performs various functions of the electronic device 7 and processes data. For example, when the at least one processor 72 executes the computer program stored in the memory, it implements all or part of the steps of the method for detecting the glue path based on 3D data described in the embodiments of the present application; or implements all or part of the functions of the device for detecting the glue path based on 3D data. The at least one processor 72 may be composed of integrated circuits. For example, it may be composed of a single packaged integrated circuit, or may be composed of multiple integrated circuits with the same or different functions packaged together, including a combination of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips, etc.

[0105] In some embodiments, the at least one communication bus 73 is configured to enable connection communication between the memory 71 and the at least one processor 72, etc. Although not shown, the electronic device 7 may further include a power supply (such as a battery) for powering each component. Preferably, the power supply can be logically connected to the at least one processor 72 through a power management device, so as to implement functions such as management of charging, discharging, and power consumption management through the power management device. The power supply may also include any components such as one or more DC or AC power supplies, a recharge device, a power failure detection circuit, a power converter or inverter, and a power status indicator. The electronic device 7 may also include various sensors, a Bluetooth module, a Wi-Fi module, etc., which will not be elaborated here.

[0106] The integrated unit implemented in the form of the above software function module can be stored in a computer-readable storage medium. The above software function module is stored in a storage medium and includes several instructions for causing an electronic device (which may be a personal computer, an electronic device, or a network device, etc.) or a processor to execute part of the methods described in the various embodiments of the present application.

[0107] In several embodiments provided in the present application, it should be understood that the disclosed device and method can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0108] The module described as a separation component may or may not be physically separated. The component shown as a module may or may not be a physical unit, and it may be located in one place or distributed across multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

Claims

1. A glue path detection method based on 3D data, characterized in that: The method comprises: Determine the glue path template according to the glue path information of the object to be tested; Locating the glue path area of ​​the object to be tested based on the 3D data of the object to be tested and the glue path template; Using a Blob tool to perform detection based on the glue path area to obtain a first detection result; When the first detection result is that the glue path is normal, subdividing the glue path area into a plurality of sub-glue path areas; Obtain multiple glue heights and multiple glue widths of each sub-glue path area; A second detection result is obtained by performing detection based on multiple glue heights and multiple glue widths of each sub-glue path area.

2. The glue path detection method based on 3D data according to claim 1 is characterized in that: The glue path template includes a preset standard glue path area and a preset standard glue path profile, and the use of the Blob tool to perform detection based on the glue path area to obtain a first detection result includes: Using a Blob tool to perform detection based on the glue path area to obtain a Blob area; Get the number of Blob regions; Determine whether the quantity is 1; When the number is 1, the glue path area of ​​the Blob region is calculated; Determining whether the glue path area is equal to the preset standard glue path area; When the glue path area is equal to the preset standard glue path area, obtaining the glue path contour of the Blob area; Calculating the matching degree between the glue path profile and the preset standard glue path profile; Determining whether the matching degree is greater than a preset matching degree threshold; When the matching degree is greater than the preset matching degree threshold, a first detection result indicating that the glue path is normal is obtained.

3. The glue path detection method based on 3D data according to claim 2 is characterized in that: The step of obtaining a plurality of glue heights and a plurality of glue widths of each sub-glue path area comprises: Each sub-glue path area is cut into sections at preset intervals to obtain multiple sections; Measuring the distance between each of the multiple cross sections and the reference plane to obtain multiple glue heights; The width of the glue groove corresponding to each of the multiple cross sections is measured to obtain multiple glue widths.

4. The glue path detection method based on 3D data according to claim 2 is characterized in that: The glue path template includes a preset standard glue height and a preset standard glue width. The detection is performed based on multiple glue heights and multiple glue widths of each sub-glue path area, and the second detection result obtained includes: For each sub-glue path area, each glue height of the plurality of glue heights is compared with the preset standard glue height to obtain a glue height detection result; Comparing each glue width of the plurality of glue widths with the preset standard glue width to obtain a glue width detection result; A second detection result is obtained according to the glue height detection result and the glue width detection result.

5. The glue path detection method based on 3D data according to claim 4 is characterized in that: The method of locating the glue path area of ​​the object to be tested based on the 3D data of the object to be tested and the glue path template includes: Obtaining glue path points in the glue path template; Finding a calibration coordinate system corresponding to the glue path information; Obtaining pixel coordinates corresponding to the glue path points in the 3D data; According to the calibration coordinate system, converting the pixel coordinates into mechanical coordinates; Positioning the object to be measured according to the mechanical coordinates and positioning the glue path area of ​​the object to be measured.

6. The method for detecting a glue path based on 3D data according to claim 5, characterized in that: The step of subdividing the glue path area into a plurality of sub-glue path areas comprises: Obtaining the accuracy requirement of the object to be measured; The glue path area is subdivided into a plurality of sub-glue path areas according to the accuracy requirement.

7. The method for detecting a glue path based on 3D data according to any one of claims 1 to 6, characterized in that: The method further comprises: Determine the target acquisition device type according to the glue material type in the glue path information; The 3D data of the object to be measured is acquired using an acquisition device associated with the target acquisition device type.

8. A glue path detection device based on 3D data, characterized in that: The device comprises: A template determination module, used to determine the glue path template according to the glue path information of the object to be tested; A glue path positioning module, used for positioning the glue path area of ​​the object to be tested based on the 3D data of the object to be tested and the glue path template; A first detection module, used for performing detection based on the glue path area using a Blob tool to obtain a first detection result; An area subdivision module, used for subdividing the glue path area into a plurality of sub-glue path areas when the first detection result is that the glue path is normal; A height and width acquisition module is used to obtain multiple glue heights and multiple glue widths of each sub-glue path area; The second detection module is used to perform detection based on multiple glue heights and multiple glue widths of each sub-glue path area to obtain a second detection result.

9. An electronic device, characterized in that: It comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the glue path detection method based on 3D data as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the glue path detection method based on 3D data as described in any one of claims 1 to 7 are implemented.

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