Intelligent mold quality detection method and system, equipment and medium

Abstract

The invention provides an intelligent mold quality detection method and system, equipment and a medium, and belongs to the technical field of quality detection.The method comprises the steps that a robot is controlled to obtain a first mold image of a to-be-detected mold on a mechanical rotary table; obtaining a target mold image and target mold information corresponding to the target mold image from a mold database based on the first mold image; the target mold information comprises mold scanning control parameters, mold types and mold label information; controlling a robot to scan a to-be-detected mold on a mechanical turntable based on the mold scanning control parameters to obtain mold scanning data, and determining a mold volume value and mold three-dimensional contour data based on the mold scanning data; and obtaining image data of the mold based on the mold label information, and determining a mold quality detection result based on the image data, the mold volume value and the mold three-dimensional contour data. According to the invention, high-precision quality detection of diversified glass molds can be realized.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of quality detection, and more particularly to a kind of intelligent mold quality detection method, system, equipment and medium. BACKGROUND

[0002] In the field of glass product production, glass mold is the core tool that determines the product shape, volume and surface precision, and its quality directly affects the function and appearance compliance of glass bottles and other products, so professional mold quality detection equipment is needed to accurately detect the mold.

[0003] With the increasing demand for personalization in the glass product market, mold detection requirements are higher. In the prior art, mold detection relies on tool measurement of key dimensions of the mold, and image recognition of contour lines to generate detection reference. However, this approach not only has low efficiency, but also has high size measurement deviation and large image recognition error, resulting in insufficient reliability of the detection results, and prone to misjudgment of unqualified molds, increasing production costs and quality risks.

[0004] Therefore, an intelligent mold quality detection method is needed to further improve the mold quality detection accuracy while ensuring detection efficiency. SUMMARY

[0005] The purpose of the present application is to provide an intelligent mold quality detection method, system, equipment and medium to further improve the mold quality detection accuracy while ensuring the detection efficiency.

[0006] The first aspect of the embodiment of the present application provides an intelligent mold quality detection method, which is executed by a control terminal device, and the control terminal device is in communication connection with a robot and a mechanical turntable respectively; the mechanical turntable is used to place a mold to be detected, and the robot is used to collect data of the mold to be detected placed on the mechanical turntable and send the collected data to the control terminal device; the control terminal device is used to control the working state of the robot and the mechanical turntable, and obtain a mold quality detection result based on the data sent by the robot; The method comprises: In response to receiving a mold detection instruction, the robot acquires a first mold image of the mold to be detected on the mechanical turntable; in response to receiving the first mold image sent by the robot, the target mold image and the target mold information corresponding to the target mold image are obtained from the mold database based on the first mold image; the target mold image is an image matched with the first mold image, and the target mold information includes mold scanning control parameters, mold type and mold label information; The robot is controlled based on the mold scanning control parameters to scan the mold to be detected on the mechanical turntable to obtain mold scanning data, and the mold volume value and the mold three-dimensional contour data are determined based on the mold scanning data; If the mold type is a glass mold containing an image, image data of the mold is obtained based on the mold label information, and a mold quality detection result is determined based on the image data, the mold volume value, and the mold three-dimensional profile data; if the mold type is a glass mold not containing an image, the mold quality detection result is determined based on the mold volume value and the mold three-dimensional profile data.

[0007] In a second aspect, the embodiment of the present application provides an intelligent mold quality detection system, which is applied to a control terminal device, and the control terminal device is in communication connection with a robot and a mechanical turntable; the mechanical turntable is used for placing a mold to be detected, the robot is used for collecting data of the mold to be detected placed on the mechanical turntable and sending the collected data to the control terminal device; and the control terminal device is used for controlling working states of the robot and the mechanical turntable and obtaining a mold quality detection result based on the data sent by the robot. The system comprises: A mold image recognition module is configured to, in response to receiving a mold detection instruction, control the robot to obtain a first mold image of a mold to be detected on the mechanical turntable, in response to receiving the first mold image sent by the robot, obtain a target mold image and target mold information corresponding to the target mold image from a mold database based on the first mold image, the target mold image is an image matched with the first mold image, and the target mold information comprises mold scanning control parameters, a mold type, and mold label information. A quality detection module is configured to control the robot to scan the mold to be detected on the mechanical turntable based on the mold scanning control parameters to obtain mold scanning data, and determine a mold volume value and mold three-dimensional profile data based on the mold scanning data. A quality analysis module is configured to, if the mold type is a glass mold containing an image, obtain image data of the mold based on the mold label information, and determine a mold quality detection result based on the image data, the mold volume value, and the mold three-dimensional profile data; if the mold type is a glass mold not containing an image, determine the mold quality detection result based on the mold volume value and the mold three-dimensional profile data.

[0008] In a third aspect, the embodiment of the present application provides a control terminal device, which comprises a memory, a processor, and a computer program stored in the memory and running on the processor, and the processor implements the steps of the intelligent mold quality detection method when executing the computer program.

[0009] In a fourth aspect, the embodiment of the present application provides a computer readable storage medium, which stores a computer program, and the computer program implements the steps of the intelligent mold quality detection method when executed by a processor.

[0010] The intelligent mold quality detection method and system, device and medium provided by the embodiment of the application have the beneficial effects that: The embodiment of the application automatically controls the robot to obtain the first mold image through the control terminal device, and matches the mold scanning control parameters, the mold type and the mold label information from the database based on the image, so that the control terminal device can automatically identify the mold to be detected and the related information; at the same time, the robot and the mechanical turntable cooperatively scan according to the preset parameters, automatically complete data acquisition and analysis, shorten the time consumption of single mold detection, quickly respond to the mold detection demand, and improve the automation level and the mold quality detection efficiency.

[0011] The embodiment of the application accurately controls the scanning process based on the mold scanning control parameters, and determines the mold volume value and the three-dimensional profile data based on the mold scanning data, so that the error can be reduced through this standardized step. At the same time, considering that part of the mold has local images, the traditional detection method cannot effectively detect the images, so the embodiment of the application divides the mold into two categories including images and not including images, carries out differentiated quality detection processes for the two types of molds, automatically switches the detection logic according to the mold type, improves the overall quality detection precision of the mold including images, and makes the application widely applicable to the quality detection of various glass molds and meet diversified needs.

[0012] In summary, the embodiment of the application realizes the automation and intelligentization of mold quality detection through the communication connection of the control terminal device, the robot and the mechanical turntable. The robot can accurately obtain the image data of the mold to be detected, and perform high-precision scanning based on the mold scanning control parameters to obtain accurate mold volume value and mold three-dimensional profile data, thereby improving the detection efficiency and precision. BRIEF DESCRIPTION OF DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in the embodiments of the application, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor.

[0014] Figure 1 A flowchart of an intelligent mold quality detection method provided by an embodiment of the application is shown in the figure. Figure 2 A structural diagram of a mold quality detection device provided by an embodiment of the application is shown in the figure. Figure 3 A second cross-sectional profile data diagram of a mold provided by an embodiment of the application is shown in the figure. Figure 4 A structural block diagram of an intelligent mold quality detection system provided by an embodiment of the application is shown in the figure. Figure 5 A schematic block diagram of a control terminal device is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0015] In the following description, specific details are set forth such as particular system configurations, techniques, etc., in order to provide a thorough understanding of embodiments of the present application. However, persons having ordinary skill in the art will readily understand that embodiments of the present application can be practiced without these specific details. In other instances, well-known structures, devices, circuits, and processes have not been described in detail so as not to unnecessarily obscure aspects of the present application.

[0016] In order to make the purposes, technical solutions and advantages of the present application clearer, the following will be described by specific embodiments in conjunction with the accompanying drawings.

[0017] Reference will be made to Figure 1 , Figure 1 A flowchart of an intelligent mold quality detection method is provided for an embodiment of the present application. The method can be executed by a control terminal device, which is in communication connection with a robot and a mechanical turntable. The mechanical turntable is used to place a mold to be detected, and the robot is used to collect data of the mold to be detected placed on the mechanical turntable and send the collected data to the control terminal device. The control terminal device is used to control the working states of the robot and the mechanical turntable, and obtain a mold quality detection result based on the data sent by the robot. Specifically, the method can include S101 to S103.

[0018] S101: In response to receiving a mold detection instruction, the robot is controlled to obtain a first mold image of a mold to be detected on the mechanical turntable. In response to receiving the first mold image sent by the robot, a target mold image and target mold information corresponding to the target mold image are obtained from a mold database based on the first mold image. The target mold image is an image matched with the first mold image, and the target mold information includes mold scanning control parameters, a mold type and mold label information.

[0019] In the present embodiment, the target mold image is obtained from the mold database based on the first mold image, specifically including: performing initial feature extraction on the first mold image to obtain contour type features, size features and pattern features; obtaining a candidate mold image set from the mold database based on the contour type features, size features and pattern features; if the candidate mold image set includes one mold image, the mold image is taken as the target mold image; if the candidate mold image set includes at least two mold images, the similarity of the first mold image with each mold image in the candidate mold image set is calculated respectively, and the mold image with the highest similarity with the first mold image is taken as the target mold image.

[0020] In the embodiment, the robot can be a movable mechanical arm, and the robot can include a camera and a laser scanning device. The mold detection instruction is a control signal triggering the mold quality detection process, used to start the detection device to perform a preset detection action. The mold detection instruction can be an instruction issued by a worker operating on a control terminal device, and the control terminal device can receive the instruction. The first mold image is an initial visual image collected by the robot using its own camera on the mechanical turntable for detecting the mold, serving as the basic data source for mold matching. The mold database is a structured data set for storing various standard mold images and supporting information, used to provide a matching reference. The target mold information is the full amount of mold data associated with the target mold image, which can include mold scanning control parameters, mold type identification, and mold label information, etc.

[0021] The mold scanning control parameters refer to specific parameters for controlling the robot and the mechanical turntable to perform scanning actions, which can include robot movement path (such as Y-axis linear path along the mold height direction), robot movement speed (such as 0 mm / s~2 mm / s), laser scanning frequency (such as 300 Hz~800 Hz), mechanical turntable rotation speed (such as 0° / s~15° / s), etc. The mold type is based on whether the mold contains an image divided category, used to distinguish the detection logic. Each mold type corresponds to a type identification, such as "1" representing a glass mold containing an image, and "0" representing a glass mold not containing an image. The mold label information is information for uniquely identifying the mold and associated basic attributes, which can include mold unique number, mold design version, and mold applicable product model (such as 500 ml glass bottle), etc.

[0022] The contour type feature is a feature reflecting the overall shape contour of the mold, used to preliminarily distinguish the mold form, which can include contour shape, contour symmetry, and contour key inflection point number, etc. The size feature is a feature reflecting the geometric size of the mold, used to quantitatively distinguish the mold specification, which can include mold maximum diameter, mold height, key cross-sectional dimension (such as bottle mouth diameter, bottle body middle diameter), and size deviation allowable range, etc. The pattern feature is a feature reflecting the mold surface pattern attribute, which can include pattern identification (such as "0" representing no pattern, and "1" representing a pattern), pattern type, pattern position, and pattern key feature point coordinates, etc. The candidate mold image set is a mold image set obtained from the mold database based on the initial features, providing a range for further accurate matching.

[0023] The consideration behind this embodiment is to realize the automatic retrieval of mold detection parameters through image feature matching, instead of manually selecting the detection program, and improve the detection efficiency and accuracy. Specifically, this embodiment selects three types of initial features: contour type, size and pattern, because these three types of features are the most core distinguishing dimensions of glass molds. The contour determines the basic shape of the mold, the size determines the mold specifications, and the pattern determines the special appearance of the mold. The combination of the three can quickly narrow the matching range and reduce database retrieval and computing costs. This embodiment divides the number of candidate sets and obtains matching results through targeted processing. A single image directly determines the target, and multiple images are sorted by similarity to select the best one, which not only avoids the mismatch risk of single feature matching, but also ensures matching accuracy through similarity quantification. This embodiment establishes a corresponding relationship between the target mold information and the mold image, which can realize the automated process of image matching, parameter retrieval, and scanning detection, meet the needs of automated and unattended detection, and avoid errors and efficiency losses in manually input parameters.

[0024] like Figure 2 As shown, Figure 2 The following is a schematic diagram of the structure of a mold quality inspection device provided in this embodiment. For example, the mold quality inspection device may include a control terminal device 110, a robot 120, and a mechanical turntable 130. The mold to be inspected can be placed on the mechanical turntable 130, and the robot 120 can carry a laser scanning head 121. In addition, a camera can be installed on the robot 120. Taking the inspection of a 500ml glass mold containing an image as an example, the control terminal device 110 can receive the mold inspection instruction input by the staff. After the control terminal device 110 parses the mold inspection instruction, it sends a standby preparation signal to the robot 120 and the mechanical turntable 130 to confirm that the equipment is in an executable state.

[0025] Control terminal device 110 sends an image acquisition instruction to robot 120, controlling robot 120 to move its industrial camera (camera) to a preset shooting position, capture an image of the mold (i.e., a first mold image), and send it to control terminal device 110. Control terminal device 110 first grayscales the first mold image and then extracts contour type features using the Canny edge detection algorithm and the Harris corner detection algorithm. It identifies the mold as a cylindrical contour with a number of key inflection points and a high level of edge smoothness. Control terminal device 110 then converts pixels to actual dimensions (presumably 10 pixels = 1 mm) to extract dimensional features: a maximum mold diameter of 65 mm, a height of 220 mm, and a bottle mouth diameter of 32 mm. Finally, control terminal device 110 extracts pattern features from the image: a combination of lines and text on the bottom of the mold, located in the center of the bottom, with 12 key feature points.

[0026] According to the extracted initial features described above, the control terminal device 110 can generate the label of the mold: profile type = cylindrical profile; size range = diameter 60-70 mm, height 210-230 mm; pattern presence = yes. The control terminal device 110 calls the mold database indexing module to trigger database fuzzy matching with the search condition of "profile type = cylindrical profile; size range = diameter 60-70 mm, height 210-230 mm; pattern presence = 1". By comparing the features with the images in the database, 3 groups of mold images that meet the conditions are filtered out to form a candidate mold image set, and the IDs of the three groups of images are assumed to be STD-500ML-001, STD-500ML-003 and STD-500ML-007.

[0027] The control terminal device 110 identifies that the candidate mold image set contains 3 images, and enables similarity calculation. Using the feature point matching method, the similarity between the first mold image and each candidate image is calculated according to the weights of profile feature 40% + size feature 35% + pattern feature 25%: the similarity with STD-500ML-001 is 96.2%, the similarity with STD-500ML-003 is 89.5%, and the similarity with STD-500ML-007 is 87.8%. The STD-500ML-001 image with the highest similarity is selected as the target mold image, and the target image ID and matching similarity are recorded.

[0028] The control terminal device 110 retrieves the corresponding target mold information from the mold database based on the image ID of the target mold image (e.g. STD-500ML-001). Among them, the mold scanning control parameters are: robot movement path = linear path along mold height Y axis (0 mm-220 mm or 220 mm-0 mm), movement speed 0.5 mm / s, laser scanning frequency 500 Hz and turntable rotation speed 5° / s; mold type is 01 (glass mold containing image); mold label information is "mold number STD-500ML-001, design version V2.1, applicable product 500 ml glass bottle".

[0029] S102: Control the robot 120 to scan the mold to be detected on the mechanical turntable 130 based on the mold scanning control parameters to obtain mold scanning data, and determine the mold volume value and the mold three-dimensional profile data based on the mold scanning data.

[0030] In this embodiment, the mold scanning data refers to the original data obtained by the robot 120 scanning the mold, which is used for subsequent calculation of volume and profile, and can include three-dimensional point cloud data, cross-sectional profile data, etc. The mold volume value refers to the volume quantization result of the mold cavity, which is used to represent the capacity compliance, and can include positive volume, negative volume and total volume, etc. The mold three-dimensional profile data refers to the three-dimensional structure data reflecting the shape of the mold, which is used to represent the shape compliance, and can include key cross-sectional profile, surface feature point coordinates, etc.

[0031] The consideration behind this embodiment is that the mold scanning control parameters can standardize the scanning process, avoid path deviation or speed fluctuation caused by manual operation, ensure the consistency and accuracy of the scanning data, and realize the detection requirements of automation and high precision; the mold volume value is directly related to the capacity function of the mold formed product, and the mold three-dimensional profile data is directly related to the product shape adaptability, and the combination of the two can comprehensively cover the core quality detection dimensions of the mold, and improve the detection efficiency and reliability.

[0032] For example, to detect the glass bottle mold forming (the mold type to be detected is LM-C202410), the specific detection process can include: The control terminal device 110 can retrieve the scanning control parameters corresponding to the mold from the database, including the robot movement path as a linear path along the mold height direction Y axis (starting point Y=0mm, ending point Y=200mm), robot movement speed 0.5mm / s, laser scanning frequency 500Hz and mechanical turntable 130 rotation speed 5° / s.

[0033] The control terminal device 110 sends a rotation instruction to the mechanical turntable 130, and the mechanical turntable 130 rotates at a uniform speed of 5° / s to drive the mold to rotate smoothly; at the same time, the control terminal device 110 sends a scanning instruction to the robot 120, and the robot 120 carries the laser scanning head 121 to move along the preset Y axis path, and the laser scanning head 121 continuously emits laser at a frequency of 500Hz to collect three-dimensional coordinate data of the mold surface, generate mold scanning data including three-dimensional point cloud data and bottle opening transverse cross-sectional and bottle body longitudinal cross-sectional profile data, and transmit the data to the control terminal device 110 in real time and store it as 1.stl format.

[0034] The control terminal device 110 can call the measurement analysis software, taking the mold scanning data in the.stl format as input, first circumscribe the effective cavity range of the mold through the cavity region segmentation algorithm, automatically calculate the positive volume 506609.736, negative volume 0.000, and total volume 506609.736 to determine the mold volume value; wherein the positive volume refers to the volume of the effective cavity region of the mold to be detected, that is, the core space volume inside the mold that can be used to contain the glass melt and ultimately determines the capacity of the formed glass product. The negative volume refers to the invalid volume loss in the effective cavity region of the mold, that is, the space volume that cannot normally contain the glass melt due to defects such as recesses, scratches, and impurity residues on the cavity wall of the mold. The total volume refers to the actual available volume of the effective cavity of the mold, and the calculation logic is total volume = positive volume - negative volume, and the total volume is the final indicator that comprehensively reflects the actual capacity of the mold cavity.

[0035] The control terminal device 110 further extracts the three-dimensional contour features corresponding to the key dimensions such as the bottle mouth diameter and the bottle body height of the mold through the edge extraction and contour fitting algorithm, and generates mold three-dimensional contour data containing key cross-sectional contour lines and surface feature point coordinates.

[0036] S103: If the mold type is a glass mold containing an image, the image data of the mold is obtained based on the mold label information, and the mold quality detection result is determined based on the image data, the mold volume value, and the mold three-dimensional contour data; if the mold type is a glass mold not containing an image, the mold quality detection result is determined based on the mold volume value and the mold three-dimensional contour data.

[0037] In this embodiment, the image data is data containing the pattern features of the image mold surface, which is used to verify the pattern compliance, for example, can include pattern feature point coordinates and line contour data. The mold quality detection result is the mold quality determination conclusion obtained based on the detection data, which is used to represent whether the mold is qualified.

[0038] This embodiment adopts differentiated detection logic according to the difference in mold type. The consideration behind this embodiment is that the pattern of the image-containing mold directly affects the appearance of the formed product, for example, the bottom of a 500ml mold is engraved with a pattern, and missing image detection is easy to lead to false detection; the image-free mold only needs to ensure that the volume and contour meet the capacity and assembly requirements. This logic avoids missing defects in the appearance of the image-containing mold, and avoids redundant detection of the image-free mold, balancing the comprehensiveness and efficiency of detection.

[0039] For example, taking the detection of a glass mold containing an image and a glass mold not containing an image as an example, the specific implementation process can include: The control terminal device 110 extracts the mold type from the target mold information, and the detection method for the glass mold containing an image can include: The control terminal device 110 calls corresponding image data from the database based on the mold unique number in the mold label information, including bottom pattern feature point coordinates and line contour data; at the same time, the mold volume value 506609.736 and the mold three-dimensional contour data determined in the foregoing are called, the image data is compared with the pattern area in the three-dimensional contour data, it is verified whether the feature point deviation is ≤0.02mm, whether the volume value is in the standard range, and the three are qualified, and then it is judged that the mold quality detection result is qualified.

[0040] The detection mode for the glass mold without containing the image can include: The control terminal device 110 directly calls the mold volume value and the mold three-dimensional contour data, verifies whether the volume value matches the nominal capacity of the glass bottle, and whether the critical dimensions such as the mouth diameter and the body height in the three-dimensional contour data are within the tolerance range, without processing the image data, and the two indicators are qualified, then it is judged that the mold quality detection result is qualified.

[0041] The control terminal device 110 generates a mold quality detection result report according to the detection result, including a judgment conclusion, detection data and deviation details, for quality traceability.

[0042] From the above, it can be concluded that the embodiment automatically controls the robot 120 to acquire the first mold image by the control terminal device 110, and matches the mold scanning control parameters, the mold type and the mold label information from the database based on the image, so that the control terminal device 110 can automatically identify the mold to be detected and the related information; at the same time, the robot 120 and the mechanical turntable 130 cooperatively scan according to the preset parameters, automatically complete data acquisition and analysis, shorten the time consumption of single mold detection, quickly respond to the mold detection demand, and improve the automation level and the mold quality detection efficiency.

[0043] The embodiment accurately controls the scanning process based on the mold scanning control parameters, and determines the mold volume value and the three-dimensional contour data by combining the mold scanning data, which can reduce the error through this standardized step. At the same time, considering that part of the mold has local image, the traditional detection mode cannot effectively detect the image, and the embodiment of the application divides the mold into two categories including the image and the image-free, carries out differentiated quality detection process for the two kinds of molds, automatically switches the detection logic according to the mold type, improves the overall quality detection precision of the mold with image, and makes the application can be widely applied to the quality detection of various glass molds, and meets the diversified needs.

[0044] In summary, the embodiment realizes the automation and intelligence of the mold quality detection by controlling the communication connection between the terminal device 110 and the robot 120 and the mechanical turntable 130. The robot 120 can accurately obtain the image data of the mold to be detected, and perform high-precision scanning based on the mold scanning control parameters to obtain accurate mold volume values and mold three-dimensional profile data, thereby improving the detection efficiency and accuracy.

[0045] In an embodiment of the present application, before responding to the mold detection instruction, the intelligent mold quality detection method further comprises: In response to receiving the robot calibration instruction, the robot 120 is controlled to obtain the measured coordinates of the first calibration position, the measured coordinates of the second calibration position, and the measured coordinates of the third calibration position based on the calibration path parameters; the first calibration position, the second calibration position, and the third calibration position are mark points preset on the mechanical turntable 130 and not on the same straight line; and the measured coordinates correspond to the robot coordinate system; The absolute coordinates of the first calibration position, the absolute coordinates of the second calibration position, and the absolute coordinates of the third calibration position are obtained; the absolute coordinates correspond to the world coordinate system; the first calibration position is on the Z-axis in the world coordinate system, the second calibration position has a different X-axis coordinate value in the world coordinate system from the first calibration position, and the third calibration position has a different Y-axis coordinate value in the world coordinate system from the first calibration position; A first coordinate offset between the measured coordinates of the first calibration position and the absolute coordinates of the first calibration position is calculated, and a translation deviation of the robot 120 is determined based on the first coordinate offset; A first measurement vector is determined based on the measured coordinates of the first calibration position and the measured coordinates of the second calibration position, and a second measurement vector is determined based on the measured coordinates of the first calibration position and the measured coordinates of the third calibration position; A first absolute vector is determined based on the absolute coordinates of the first calibration position and the absolute coordinates of the second calibration position, and a second absolute vector is determined based on the absolute coordinates of the first calibration position and the absolute coordinates of the third calibration position; A first plane rotation deviation is calculated based on the projection of the first measurement vector on the first plane and the first absolute vector; A second plane rotation deviation is calculated based on the projection of the first measurement vector on the second plane and the first absolute vector; A third plane rotation deviation is calculated based on the projection of the second measurement vector on the third plane and the second absolute vector; The position of the robot 120 is calibrated based on the translation deviation, the first plane rotation deviation, the second plane rotation deviation, and the third plane rotation deviation; and the robot 120 after completing the position calibration is used to collect data of the mold to be detected placed on the mechanical turntable 130, and the collected data is sent to the terminal device 110.

[0046] In the embodiment, the robot calibration instruction is a control signal triggering a robot position calibration process, which is used to start the calibration action. The calibration path parameter is a preset path configuration data capable of controlling the robot 120 to move to a specified position, and the robot 120 can collect the coordinates of each calibration position at the specified position. The first calibration position, the second calibration position, and the third calibration position are non-collinear marker points preset on the mechanical turntable 130, which are used to provide a calibration reference. For example, the marker points can be made of metal (such as stainless steel), have a convex shape (such as a spherical / cylindrical shape), and have a size (such as a diameter of 5 mm). The coordinates of each marker point are recorded. The measured coordinates are the actual collected coordinates of the calibration positions in the robot coordinate system. The robot coordinate system is a three-dimensional coordinate system established with the base of the robot 120 as the reference, which is used to position the end position of the robot 120. The absolute coordinates are the standard coordinates of the calibration positions in the world coordinate system, which are used to provide a calibration reference value. The world coordinate system can be a global three-dimensional coordinate system of the mold quality detection equipment, which is used to unify the calibration positions and the position reference of the robot 120.

[0047] The first coordinate offset is the difference between the measured coordinates of the first calibration position and the absolute coordinates, which is used to calculate the translation deviation. For example, it can include an X-axis offset value, a Y-axis offset value, and a Z-axis offset value. The translation deviation is the offset of the overall position of the robot 120 relative to the standard position, which is used to represent the translation error of the robot. For example, it can include an X-axis translation value, a Y-axis translation value, and a Z-axis translation value. The first measurement vector is used to calculate the rotation deviation. The first absolute vector is used to provide a reference for calculating the rotation deviation. The first plane, the second plane, and the third plane are the X-Y plane, the X-Z plane, and the Y-Z plane, respectively, which are used to split the rotation deviation dimension. The first plane rotation deviation, the second plane rotation deviation, and the third plane rotation deviation are the angle differences of the vector projections in the corresponding planes, which are used to represent the rotation error of the robot.

[0048] The consideration behind the embodiment is that the robot 120 and the mechanical turntable 130 are active during work, and position deviations will occur after continuous and long-term work. Calibrating the robot 120 before data acquisition can establish a unified and accurate spatial coordinate reference for the robot 120 and the mechanical turntable 130, eliminate position deviations caused by equipment installation deviations, mechanical wear, and environmental disturbances (such as temperature deformation), and ensure the accuracy of subsequent mold data acquisition (image acquisition, scanning detection).

[0049] For example, the robot 120 is prone to translational deviation (e.g., 0.05 mm overall deviation in the X-axis) or rotational deviation (e.g., 0.02° tilt around the Y-axis) due to base loosening and joint wear during long-term use. If not calibrated, the laser scanning head 121 will deviate from the preset scanning area, resulting in misalignment of the mold scanning data. Calibration can ensure that the volume value and three-dimensional profile data collected by the robot 120 are consistent with the actual mold shape, avoiding false positives or false negatives of qualified molds. The present embodiment has a robot coordinate system and a world coordinate system, and calibration can establish a precise mapping relationship between the two. For example, the three non-collinear calibration positions preset by the mechanical turntable 130 have fixed absolute coordinates. By calibrating the measurement coordinates collected by the robot 120 with the absolute coordinates, it is ensured that when the robot 120 and the mechanical turntable 130 cooperate in scanning, their motion trajectories strictly match without timing or positional deviation.

[0050] Considering that two points can only determine a straight line and cannot cover three-dimensional rotational errors, the present embodiment selects three non-collinear calibration positions, which can construct a complete three-dimensional coordinate system to ensure that the deviation calculation covers all dimensions of translation and rotation, meeting the requirement of full-space precision guarantee in the purpose; considering that the robot error is essentially a combination of overall positional deviation and attitude tilt, the present embodiment separates the translational deviation and the three-plane rotational deviation. This classification calculation can accurately locate the error source and avoid the lack of precision caused by general correction. For example, the robot calibration before detecting a 500 ml glass mold containing an image is as follows: The control terminal device 110 triggers the calibration process after receiving the robot calibration instruction. The control terminal device 110 sends a static instruction to the mechanical turntable 130. The control terminal device 110 retrieves the preset calibration path parameters from the system database and simultaneously sends a calibration instruction to the robot 120. The robot 120 moves along the preset calibration path with the laser scanning head 121. When it reaches the first calibration position, it stops for 1 s and collects its measurement coordinates in the robot coordinate system. The second calibration position and the third calibration position are sequentially completed, and the measurement coordinates are sent to the control terminal device 110.

[0051] The control terminal device 110 retrieves the absolute coordinates of the three calibration positions from the world coordinate system parameter library and converts the absolute coordinates and the measurement coordinates to the same coordinate system. The first coordinate offset of the first calibration position is calculated after the conversion: 0.2 mm in the X-axis, 0.1 mm in the Y-axis, and 0 mm in the Z-axis. The control terminal device 110 determines this offset as the translational deviation of the robot. This translational deviation can reflect the positional deviation caused by the loosening of the robot base and provide data support for subsequent translational correction.

[0052] The control terminal device 110 calculates a first measurement vector and a second measurement vector based on the measured coordinates, and calculates a first absolute vector and a second absolute vector based on the absolute coordinates. The direction and distance relationship between the calibration positions can be characterized by vectors, which provides data for the direction dimension of the rotation deviation calculation and avoids the defect that a single coordinate comparison cannot reflect the rotation error.

[0053] The control terminal device 110 projects the first measurement vector to the X-Y plane (the Z component is 0), compares it with the first absolute vector, and calculates a first plane rotation deviation (such as 0.1°). The control terminal device 110 projects the first measurement vector to the X-Z plane (the Y component is 0), compares it with the first absolute vector, and calculates a second plane rotation deviation of -0.5°. The control terminal device 110 projects the second measurement vector to the Y-Z plane (the X component is 0), compares it with the second absolute vector, and calculates a third plane rotation deviation of 0.8°. This step can split the rotation error in the three-dimensional space into three orthogonal planes, avoiding the cross interference of errors in different dimensions and ensuring the accuracy of the rotation deviation calculation.

[0054] The control terminal device 110 sends the translation deviation and the three plane rotation deviations to the robot 120, and the robot 120 automatically adjusts the joint parameters of the robot according to the translation deviation and the three plane rotation deviations. After adjustment, the robot 120 collects the measurement coordinates of the three calibration positions again until the deviation from the absolute coordinates is less than or equal to 0.01 mm (satisfying the threshold requirement), and then determines that the calibration is completed.

[0055] The embodiment eliminates the translation deviation and the rotation deviation of the robot 120 caused by long-term use, ensures the accurate alignment of the laser scanning head 121 to the scanning area of the mold, avoids the misplacement of the scanning data, and makes the subsequent collected mold volume values and three-dimensional contour data consistent with the actual mold shape, thereby improving the accuracy of data acquisition. In the calibration process, the mapping relationship between the robot coordinate system and the world coordinate system is established by comparing the measurement coordinates of the calibration positions with the absolute coordinates, and the motion trajectory is strictly matched when the robot 120 and the mechanical turntable 130 cooperate in scanning, thereby providing a stable cooperative reference for subsequent mold full-surface scanning and image acquisition.

[0056] In an embodiment of the present application, the mold scanning control parameters include a robot movement path, a robot movement speed, a laser scanning frequency, and a turntable rotation speed. The robot 120 is controlled based on the mold scanning control parameters to scan the mold to be detected on the mechanical turntable 130 to obtain mold scanning data, including: The mechanical turntable 130 is controlled to rotate based on the turntable rotation speed; The robot 120 is controlled to scan the mold to be detected on the mechanical turntable 130 based on the robot scanning path, the robot movement speed, and the laser scanning frequency, and three-dimensional point cloud data, first cross-sectional contour data, and second cross-sectional contour data are sequentially obtained; The three-dimensional point cloud data is a set of discrete three-dimensional coordinates of the full surface of the mold, the first cross-sectional profile data is a circular profile of a horizontal cross section of a mouth of the mold, and the second cross-sectional profile data is a continuous curve of a vertical symmetrical cross section of a body of the mold.

[0057] In the embodiment, the mold volume value and the mold three-dimensional profile data are determined based on the mold scanning data, specifically including: constructing a target coordinate system based on the first cross-sectional profile data and the second cross-sectional profile data; generating a mold three-dimensional model based on the target coordinate system, the three-dimensional point cloud data, the first cross-sectional profile data and the second cross-sectional profile data; determining the mold volume value and the mold three-dimensional profile data based on the mold three-dimensional model.

[0058] In the embodiment, the target coordinate system is constructed based on the first cross-sectional profile data and the second cross-sectional profile data, specifically including: determining a mouth center position based on the first cross-sectional profile data; taking the mouth center position as an origin of the target coordinate system; extracting a body center line based on the second cross-sectional profile data, an upper end point of the body center line being the mouth and a lower end point of the body center line being a bottom, taking a direction from the upper end point to the lower end point of the body center line as a positive direction of a Y-axis of the target coordinate system; and taking a right direction perpendicular to the Y-axis as a positive direction of an X-axis of the target coordinate system.

[0059] In the embodiment, the target coordinate system is a local three-dimensional coordinate system constructed based on key cross-sectional profiles of the mold, used to unify the coordinate reference of the mold scanning data and ensure the coordinate consistency of subsequent modeling and data calculation, the origin and the axis system direction of which are determined based on the characteristics of the mold itself rather than relying on the equipment reference. The body center line is a center line extracted from the second cross-sectional profile data, penetrating through the vertical symmetrical cross section of the body, the upper end point of which corresponds to the mouth and the lower end point of which corresponds to the bottom, representing the vertical symmetrical reference of the mold and used to define the Y-axis direction of the target coordinate system. The mold three-dimensional model is a digital mold model generated by fusing the three-dimensional point cloud data and the first cross-sectional profile data and the second cross-sectional profile data based on the target coordinate system, directly reflecting the geometric shape of the full surface of the mold and being the basis for calculating the mold volume value and extracting the three-dimensional profile data. The circular profile is a discrete point cluster constituting the circular profile of the horizontal cross section of the mouth, representing the horizontal geometric shape of the mouth and providing data support for determining the mouth center position. The continuous curve is a point cluster constituting the continuous curve of the vertical symmetrical cross section of the body, the points being arranged continuously, accurately reflecting the vertical bending shape of the body and providing data basis for extracting the body center line.

[0060] The first cross-sectional profile (transverse to the bottle mouth) and the second cross-sectional profile (longitudinal to the bottle body) are selected to construct the target coordinate system in this embodiment. The center of the bottle mouth and the centerline of the bottle body are inherent symmetrical features of the glass mold, which are stable in position and easy to extract from the scanning data. This can avoid the cumulative error when taking the equipment coordinate system as the reference, and ensure that the coordinate system is strongly related to the shape of the mold itself, providing a reliable reference for subsequent modeling. In this embodiment, the turntable is controlled to rotate first, and then the robot 120 scans. This is because the rotation of the turntable can drive the circumferential surface of the mold to be exposed in turn, combined with the movement of the robot 120 along the height direction of the bottle body, the full surface of the mold can be covered without blind area. In this embodiment, the three-dimensional point cloud and the cross-sectional profile data are combined to generate a three-dimensional model. This is because the three-dimensional point cloud can provide full surface details, and the cross-sectional profile data can ensure the accuracy of the key shape. The two complement each other to balance the modeling accuracy and efficiency, avoiding redundant calculation when only using point cloud modeling or missing details when only using profile modeling. In this embodiment, three types of data are split as scanning data, because different data correspond to different functions: two types of cross-sectional profiles are used to define the reference, and the point cloud is used to complete the surface. Classification processing can improve data processing efficiency.

[0061] In this embodiment, the robot scanning path includes a first scanning path, a second scanning path, a third scanning path, and a fourth scanning path; the laser scanning frequency includes a first laser scanning frequency and a second laser scanning frequency, and the first laser scanning frequency is greater than the second laser scanning frequency; Based on the robot scanning path, the robot movement speed and the laser scanning frequency, the robot 120 is controlled to scan the mold to be detected on the mechanical turntable 130, and three-dimensional point cloud data, first cross-sectional profile data and second cross-sectional profile data are obtained in turn. Specifically, it includes: Based on the first scanning path, the robot movement speed and the first laser scanning frequency, the robot 120 is controlled to scan the mold to be detected on the mechanical turntable 130, and three-dimensional point cloud data is obtained. The starting point of the first scanning path corresponds to the bottle bottom data collection point of the mold to be detected, and the ending point of the first scanning path corresponds to the first bottle mouth data collection point of the mold to be detected. The first bottle mouth data collection point is at the same horizontal plane as the bottle mouth of the mold to be detected; Based on the second scanning path and the robot movement speed, the robot 120 is controlled to move to the second bottle mouth data collection point of the mold to be detected; based on the second laser scanning frequency, the robot 120 is controlled to scan the bottle mouth of the mold to be detected on the mechanical turntable 130, and the first cross-sectional profile data is obtained. The starting point of the second scanning path is the first bottle mouth data collection point, and the ending point of the second scanning path is the second bottle mouth data collection point, which is located above the bottle mouth of the mold to be detected; The mechanical turntable 130 is controlled to stop rotating; The robot 120 is controlled to move to the first bottle mouth data collection point of the mold to be detected based on the third scanning path and the robot moving speed, and the starting point of the third scanning path is the second bottle mouth data collection point, and the ending point of the third scanning path is the first bottle mouth data collection point. The robot 120 is controlled to scan the mold to be detected on the mechanical turntable 130 based on the fourth scanning path, the robot moving speed and the second laser scanning frequency, and the starting point of the fourth scanning path is the first bottle mouth data collection point, and the ending point of the fourth scanning path is the bottle bottom data collection point.

[0062] For example, after the control terminal device 110 receives the mold detection instruction, the control terminal device 110 retrieves the mold scanning control parameters corresponding to the mold from the mold database: the turntable rotating speed is set to 5° / s; the robot moving speed is set to 0.5 mm / s; the first laser scanning frequency is set to 500 Hz (for high-frequency sampling of three-dimensional point cloud), and the second laser scanning frequency is set to 200 Hz (for medium-high-frequency sampling of cross-section profile). At the same time, the control terminal device 110 retrieves the scanning path parameters of the robot 120: the starting point of the first scanning path is the bottle bottom data collection point corresponding to the center of the mold bottle bottom, and the preset Y=210 mm, and the ending point is the first bottle mouth data collection point which is in the same horizontal plane as the bottle mouth and Y=0 mm; the starting point of the second scanning path is the first bottle mouth data collection point, Y=0 mm, and the ending point is the second bottle mouth data collection point which is 10 cm above the bottle mouth, Y=-10 cm; the starting point of the third scanning path is the second bottle mouth data collection point Y=-10 cm, and the ending point is the first bottle mouth data collection point Y=0 mm; the starting point of the fourth scanning path is the first bottle mouth data collection point, and the ending point is the bottle bottom data collection point.

[0063] The control terminal device 110 sends a rotating instruction to the mechanical turntable 130, and the turntable drives the mold to rotate at a uniform speed of 5° / s. The servo motion mechanism of the mechanical platform ensures that the shaking amplitude of the turntable is less than or equal to 0.001 mm, so as to avoid the offset of the mold, and the circumferential surface of the mold enters the scanning range of the laser scanning head 121 in turn.

[0064] Three-dimensional point cloud data collection: the control terminal device 110 sends a first scanning instruction to the robot 120, and the robot 120 carries the laser scanning head 121 to move along the first scanning path: from the bottle bottom data collection point to the first bottle mouth data collection point at a speed of 0.5 mm / s, and the laser scanning head 121 continuously samples at the first laser scanning frequency (500 Hz), collects one three-dimensional coordinate point every 0.002 seconds, covers the whole surface of the bottle bottom, the bottle body and the bottle mouth, accumulatively collects about 1.1 million discrete points, forms three-dimensional point cloud data, and stores the three-dimensional point cloud data in the.stl format, so as to ensure that the details such as the corner arc of the mold and the bottle bottom groove are restored.

[0065] Robot 120 moves to the second bottle mouth data collection point: after the completion of three-dimensional point cloud data collection, the control terminal device 110 sends a second scanning instruction, and the robot 120 moves along a second scanning path: from the first bottle mouth data collection point to the second bottle mouth data collection point at a speed of 0.5 mm / s, in this process, the laser scanning head 121 stops sampling, and only through the servo positioning of the robot joint, the center of the scanning head is aligned with the center of the bottle mouth transverse section.

[0066] First cross-sectional profile data collection: after the robot 120 reaches the second bottle mouth data collection point, the control terminal device 110 instructs the laser scanning head 121 to start scanning at a second laser scanning frequency (200 Hz), and the turntable still rotates at 5° / s, which exposes the bottle mouth transverse section completely, collects a coordinate point every 0.005 seconds, accumulates 300 points, forms the first cross-sectional profile data of the bottle mouth transverse circular profile, and stores it in the.csv format.

[0067] Mechanical turntable 130 stops rotating: after the completion of the first cross-sectional profile collection, the control terminal device 110 sends a stop instruction, and the turntable is quickly stationary through the servo brake mechanism, and the angle deviation after being stationary is ≤0.01°, which avoids the mold offset during the subsequent longitudinal section collection.

[0068] Robot 120 moves back to the first bottle mouth data collection point: the control terminal device 110 sends a third scanning instruction, and the robot 120 moves along a third scanning path: from the second bottle mouth data collection point to the first bottle mouth data collection point at a speed of 0.5 mm / s, and through the encoder feedback, it is ensured that the scanning direction coincides with the longitudinal symmetrical section of the bottle body.

[0069] Second cross-sectional profile data collection: after the robot 120 reaches the first bottle mouth data collection point, the control terminal device 110 sends a fourth scanning instruction, and the robot 120 moves along a fourth scanning path: from the first bottle mouth data collection point to the bottle bottom data collection point at a speed of 0.5 mm / s, and the laser scanning head 121 samples at the second laser scanning frequency, forming the second cross-sectional profile data of the longitudinal continuous curve of the bottle body, which is stored in the.csv format. As shown in Figure 3 Figure 3 is the second cross-sectional profile obtained by the plurality of molds during the detection process.

[0070] ​The robot 120 transmits the three types of data obtained in real time to the control terminal device 110. The control terminal device 110 performs circular fitting on the first cross-sectional profile data, calculates the bottle mouth center position (set as X=0, Y=0, Z=0) by analyzing the coordinate distribution of the circular profile point set, and sets the position as the origin of the target coordinate system; the control terminal device 110 performs symmetry analysis on the second cross-sectional profile data, extracts the bottle body center line running through the bottle body, determines the upper end point (bottle mouth center) and lower end point (bottle bottom center) on the center line, and sets the direction of the center line from the upper end point to the lower end point as the positive direction of the Y axis; the control terminal device 110 sets the direction perpendicular to the Y axis and pointing to the right side of the bottle body as the positive direction of the X axis, and the Z axis direction is determined according to the right-hand rule, thereby completing the construction of the target coordinate system.

[0071] The control terminal device 110 maps the three-dimensional point cloud data to the target coordinate system based on the target coordinate system, and integrates the first cross-sectional profile data and the second cross-sectional profile data, to finally generate a mold three-dimensional model. The control terminal device 110 circumscribes the mold effective cavity type area based on the mold three-dimensional model, automatically calculates the cavity type positive volume (set as 499750 The control terminal device 110 extracts the key profile features of the mold full surface from the mold three-dimensional model to form mold three-dimensional profile data, and completes data determination.

[0072] The embodiment constructs a target coordinate system based on the key cross sections of the mold itself, avoids the cumulative deviation of the device coordinate system, and avoids quality misjudgment caused by reference deviation. The embodiment cooperates with the rotation of the turntable and the movement of the robot 120, combines laser high-frequency sampling, can cover the circumferential surface and longitudinal surface of the mold, avoids blind areas such as bottle body corners and bottle bottom inner side, and improves the measurement efficiency and accuracy.

[0073] In an embodiment of the present application, the mold quality detection result is determined based on the image data, the mold volume value and the mold three-dimensional profile data, including: obtaining the template image data corresponding to the target mold image, the standard volume value and the standard three-dimensional profile data; calculating the image similarity of the image data and the template image data; calculating the volume difference value of the mold volume value and the standard volume value; calculating the three-dimensional profile similarity of the mold three-dimensional profile data and the standard three-dimensional profile data; determining the mold quality detection result based on the image similarity, the volume difference value and the three-dimensional profile similarity.

[0074] ​In the embodiment, the template image data is the standard image data corresponding to the target mold image, is the reference for image data comparison, and is used to verify the compliance of the appearance features of the mold. The standard volume value is the standard volume determined in the design stage of the target mold, is the reference for mold volume value comparison, reflects the preset capacity of the molded product of the mold, and is used to verify the compliance of the function of the mold. The standard three-dimensional contour data is the standard shape data of the target mold, is the reference for mold three-dimensional contour data comparison, and is used to verify the compliance of the shape of the mold. The image similarity is a quantitative index of the similarity degree of the image data and the template image data, which can be calculated by comparing pattern feature points, lines and the like, for example, and represents the compliance degree of the appearance features such as the surface pattern of the mold. The volume difference is the difference between the mold volume value and the standard volume value, directly reflects the deviation of the mold capacity from the standard value, and represents the compliance degree of the capacity function of the mold. The three-dimensional contour similarity is a quantitative index of the similarity degree of the mold three-dimensional contour data and the standard three-dimensional contour data, which can be calculated by comparing key contour features, sizes and the like, for example, and represents the compliance degree of the shape of the mold.

[0075] For example, the control terminal device 110 retrieves the standard data corresponding to the mold of this model from the mold database: the template image data (including the standard pattern of the bottle bottom logo), the standard volume value (500 ml, corresponding to the volume 500000 ), and the standard three-dimensional contour data (including the standard diameter of the bottle mouth 32.0 mm, the standard height of the bottle body 220.0 mm, and the maximum diameter of the bottle body 65.0 mm). The control terminal device 110 calls the image comparison module, aligns the actual pattern (image data) of the bottle bottom of the mold to be detected with the template image data, calculates the image similarity through image recognition, and obtains the result as 98.6%. The control terminal device 110 retrieves the mold volume value (499750 ) of the mold to be detected determined in the foregoing, directly calculates the difference with the standard volume value (500000 ), and obtains the volume difference as -250 , and the absolute value as 250 .

[0076] The control terminal device 110 maps the three-dimensional contour data of the mold to be detected to the same target coordinate system as the standard three-dimensional contour data, compares the key size deviations such as the diameter of the bottle mouth (actual 32.03 mm, standard 32.0 mm) and the height of the bottle body (actual 219.95 mm, standard 220.0 mm), and calculates the three-dimensional contour similarity in combination with the contour edge smoothness, and obtains the result as 99.3%.

[0077] The control terminal device 110 presets the quality judgment threshold: the image similarity is greater than or equal to 95%, and the absolute value of the volume difference is less than or equal to 500 Three-dimensional profile similarity ≥98%. Compare the three calculated indicators with the threshold value: image similarity 98.6% ≥95%, volume difference absolute value 250 ≤500 Three-dimensional profile similarity 99.3% ≥98%, all indicators meet the requirements, and the mold quality detection result is determined to be qualified, and a detection report containing the values of each indicator is generated.

[0078] This embodiment covers mold quality requirements from three dimensions of image, volume and three-dimensional profile, avoiding missed judgment caused by detecting only a single dimension. This embodiment quantifies the detection results by image similarity, volume difference and three-dimensional profile similarity, improving the credibility of the detection results.

[0079] An intelligent mold quality detection method corresponding to the above embodiment, Figure 4 A structural block diagram of an intelligent mold quality detection system provided by an embodiment of the present application. For ease of illustration, only parts related to the embodiments of the present application are shown. For reference Figure 4 The intelligent mold quality detection system 20 is applied in a control terminal device, which is in communication connection with a robot and a mechanical turntable respectively; the mechanical turntable is used to place a mold to be detected, and the robot is used to collect data of the mold to be detected placed on the mechanical turntable and send the collected data to the control terminal device; the control terminal device is used to control the working states of the robot and the mechanical turntable, and obtain a mold quality detection result based on the data sent by the robot; the intelligent mold quality detection system 20 comprises a mold image recognition module 21, a quality detection module 22 and a quality analysis module 23.

[0080] The mold image recognition module 21 is used to control the robot to obtain a first mold image of the mold to be detected on the mechanical turntable in response to receiving a mold detection instruction; in response to receiving the first mold image sent by the robot, the target mold image and the target mold information corresponding to the target mold image are obtained from the mold database based on the first mold image; the target mold image is an image matched with the first mold image, and the target mold information includes mold scanning control parameters, mold type and mold label information; The quality detection module 22 is used to control the robot to scan the mold to be detected on the mechanical turntable based on the mold scanning control parameters to obtain mold scanning data, and determine the mold volume value and the mold three-dimensional profile data based on the mold scanning data; The quality analysis module 23 is configured to: if the mold type is a glass mold containing an image, acquire image data of the mold based on the mold label information, and determine a mold quality detection result based on the image data, the mold volume value and the mold three-dimensional profile data; if the mold type is a glass mold not containing an image, determine the mold quality detection result based on the mold volume value and the mold three-dimensional profile data.

[0081] In an embodiment of the present application, the intelligent mold quality detection system 20 further comprises: The robot calibration module is configured to: in response to receiving a robot calibration instruction, control the robot to acquire a measured coordinate of a first calibration position, a measured coordinate of a second calibration position and a measured coordinate of a third calibration position based on calibration path parameters; the first calibration position, the second calibration position and the third calibration position are mark points preset on the mechanical turntable and not on the same straight line; the measured coordinates correspond to a robot coordinate system; acquire an absolute coordinate of the first calibration position, an absolute coordinate of the second calibration position and an absolute coordinate of the third calibration position; the absolute coordinates correspond to a world coordinate system; the first calibration position is on the Z-axis in the world coordinate system, the second calibration position has a different X-axis coordinate value in the world coordinate system from the first calibration position, and the third calibration position has a different Y-axis coordinate value in the world coordinate system from the first calibration position; calculate a first coordinate offset between the measured coordinate of the first calibration position and the absolute coordinate of the first calibration position, and determine a translation deviation of the robot based on the first coordinate offset; determine a first measurement vector based on the measured coordinate of the first calibration position and the measured coordinate of the second calibration position, and determine a second measurement vector based on the measured coordinate of the first calibration position and the measured coordinate of the third calibration position; determine a first absolute vector based on the absolute coordinate of the first calibration position and the absolute coordinate of the second calibration position, and determine a second absolute vector based on the absolute coordinate of the first calibration position and the absolute coordinate of the third calibration position; calculate a first plane rotation deviation based on a projection of the first measurement vector on a first plane and the first absolute vector; calculate a second plane rotation deviation based on a projection of the first measurement vector on a second plane and the first absolute vector; calculate a third plane rotation deviation based on a projection of the second measurement vector on a third plane and the second absolute vector; calibrate the position of the robot based on the translation deviation, the first plane rotation deviation, the second plane rotation deviation and the third plane rotation deviation; the robot after completing the position calibration is used for data acquisition on the mold to be detected placed on the mechanical turntable, and sends the acquired data to the control terminal device.

[0082] In an embodiment of the present application, the mold image recognition module 21 is specifically configured to: perform initial feature extraction on the first mold image to obtain contour type features, size features and pattern features; match a candidate mold image set from the mold database based on the contour type features, the size features and the pattern features; if the candidate mold image set includes one mold image, the mold image is taken as the target mold image; if the candidate mold image set includes at least two mold images, the similarity of the first mold image and each mold image in the candidate mold image set is calculated respectively, and the mold image with the highest similarity to the first mold image is taken as the target mold image.

[0083] In an embodiment of the present application, the mold scanning control parameters include a robot movement path, a robot movement speed, a laser scanning frequency and a turntable rotation speed; the quality detection module 22 is specifically configured to: control the mechanical turntable to rotate based on the turntable rotation speed; control the robot to scan the mold to be detected on the mechanical turntable based on the robot scanning path, the robot movement speed and the laser scanning frequency, and sequentially obtain three-dimensional point cloud data, first cross-sectional contour data and second cross-sectional contour data; the three-dimensional point cloud data is a discrete three-dimensional coordinate set of the full surface of the mold, the first cross-sectional contour data is a circular contour of the horizontal cross section of the mouth of the mold, and the second cross-sectional contour data is a continuous curve of the vertically symmetrical cross section of the body of the mold; and the three-dimensional point cloud data, the first cross-sectional contour data and the second cross-sectional contour data are taken as mold scanning data.

[0084] In an embodiment of the present application, the quality detection module 22 is specifically further configured to: construct a target coordinate system based on the first cross-sectional contour data and the second cross-sectional contour data; generate a mold three-dimensional model based on the target coordinate system, the three-dimensional point cloud data, the first cross-sectional contour data and the second cross-sectional contour data; and determine a mold volume value and mold three-dimensional contour data based on the mold three-dimensional model.

[0085] In an embodiment of the present application, the quality detection module 22 is specifically further configured to: determine a bottle mouth center position based on the first cross-sectional contour data; take the bottle mouth center position as the origin of the target coordinate system; extract a bottle body center line based on the second cross-sectional contour data, the upper end point of the bottle body center line is the bottle mouth, and the lower end point is the bottle bottom, and the direction from the upper end point to the lower end point of the bottle body center line is taken as the positive direction of the Y axis of the target coordinate system; and the right direction perpendicular to the Y axis is taken as the positive direction of the X axis of the target coordinate system.

[0086] In one embodiment of the present application, the quality analysis module 23 is specifically used to: obtain template image data, standard volume value and standard three-dimensional contour data corresponding to the target mold image; calculate the image similarity between the image data and the template image data; calculate the volume difference between the mold volume value and the standard volume value; calculate the three-dimensional contour similarity between the mold three-dimensional contour data and the standard three-dimensional contour data; and determine the mold quality detection result based on the image similarity, volume difference and three-dimensional contour similarity.

[0087] See also Figure 5 , Figure 5 This is a schematic block diagram of a control terminal device provided in one embodiment of the present application. Figure 5 The control terminal device 300 in the embodiment shown may include: one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The processors 301, input devices 302, output devices 303, and memories 304 communicate with each other via a communication bus 305. The memory 304 is used to store computer programs, which include program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. The processor 301 is configured to call the program instructions to execute the functions of each module in the above-mentioned system embodiments, such as Figure 4 The functions of the mold image recognition module 21, the quality detection module 22 and the quality analysis module 23 are shown.

[0088] It should be understood that in the embodiment of the present application, the processor 301 may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0089] The input device 302 may include a touchpad, a fingerprint collection sensor (for collecting user fingerprint information and fingerprint direction information), a microphone, etc. The output device 303 may include a display (LCD, etc.), a speaker, etc.

[0090] The memory 304 can include read-only memory and random access memory, and provide instructions and data to the processor 301. A portion of the memory 304 can also include non-volatile random access memory. For example, the memory 304 can also store mold type information.

[0091] In specific implementations, the processor 301, the input device 302, and the output device 303 described in the embodiments of the present application can perform the implementation manners described in the embodiments of the intelligent mold quality detection method provided by the embodiments of the present application, and can also perform the implementation manners of the control terminal device 300 described in the embodiments of the present application, which will not be described here.

[0092] In another embodiment of the present application, a computer readable storage medium is provided, which stores a computer program. The computer program includes program instructions, which are executed by a processor to implement all or part of the processes of the above-mentioned embodiments. The computer program can also be executed by related hardware to complete the computer program. The computer program can be stored in a computer readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned method embodiments can be implemented. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer readable medium can include any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0093] The computer readable storage medium can be an internal storage unit of the control terminal device, such as a hard disk or a memory of the control terminal device. The computer readable storage medium can also be an external storage device of the control terminal device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. Further, the computer readable storage medium can include both the internal storage unit and the external storage device of the control terminal device. The computer readable storage medium is used to store the computer program and other programs and data required by the control terminal device. The computer readable storage medium can also be used to temporarily store data that has been output or will be output.

[0094] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the control terminal device and the unit described above can refer to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0095] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the control terminal device and the unit described above can refer to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0096] In several embodiments provided in the present application, it should be understood that the disclosed control terminal device and method can be implemented in other ways. For example, the system embodiments described above are merely schematic, for example, the division of the module / unit is only a logical function division, and actual implementation can have another division manner, for example, a plurality of modules, units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed each other can be indirect coupling or communication connection through some interfaces or modules / units, and can also be electrical, mechanical or other form of connection.

[0097] The module / unit described as a separate component can be or can not be physically separated, and the component displayed as a module / unit can be or can not be a physical module / unit, that is, can be located in one place, or can be distributed to a plurality of network modules / units. Part or all of the modules / units can be selected according to actual needs to achieve the purpose of the embodiments of the present application.

[0098] In addition, each functional module / unit in each embodiment of the present application can be integrated in one processing module / unit, or each module / unit can exist physically, or two or more modules / units can be integrated in one module / unit. The integrated module / unit can be realized in the form of hardware or in the form of a software functional module / unit.

[0099] The above merely provides the specific implementation of the present application, but the protection scope of the present application is not limited to this. Any person skilled in the art can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present application, and these modifications or replacements should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. An intelligent mold quality detection method, characterized in that: The method is performed by a control terminal device, which is communicatively connected to a robot and a mechanical turntable, respectively; the mechanical turntable is used to place a mold to be inspected, and the robot is used to collect data from the mold to be inspected placed on the mechanical turntable and send the collected data to the control terminal device; The control terminal device is used to control the working status of the robot and the mechanical turntable, and obtain the mold quality inspection result based on the data sent by the robot; The method comprises: In response to receiving a mold inspection instruction, controlling the robot to obtain a first mold image of the mold to be inspected on the mechanical turntable; In response to receiving a first mold image sent by the robot, obtaining a target mold image and target mold information corresponding to the target mold image from a mold database based on the first mold image; the target mold image is an image that matches the first mold image, and the target mold information includes mold scanning control parameters, mold type, and mold label information; Controlling the robot to scan the mold to be inspected on the mechanical turntable based on the mold scanning control parameters to obtain mold scanning data, and determining a mold volume value and mold three-dimensional contour data based on the mold scanning data; If the mold type is a glass mold containing an image, the image data of the mold is obtained based on the mold label information, and the mold quality inspection result is determined based on the image data, the mold volume value and the mold three-dimensional contour data; if the mold type is a glass mold not containing an image, the mold quality inspection result is determined based on the mold volume value and the mold three-dimensional contour data.

2. An intelligent mold quality detection method according to claim 1, characterized in that: Before responding to the mold detection instruction, the method further includes: In response to receiving a robot calibration instruction, controlling the robot to obtain measurement coordinates of a first calibration position, a second calibration position, and a third calibration position based on calibration path parameters; the first calibration position, the second calibration position, and the third calibration position are marker points preset on a mechanical turntable and not on the same straight line; the measurement coordinates all correspond to the robot coordinate system; Obtaining absolute coordinates of a first marker position, an absolute coordinate of a second marker position, and an absolute coordinate of a third marker position; the absolute coordinates all correspond to a world coordinate system; the first marker position is on the Z axis in the world coordinate system, the second marker position and the first marker position have different X axis coordinate values ​​in the world coordinate system, and the third marker position and the first marker position have different Y axis coordinate values ​​in the world coordinate system; Calculating a first coordinate offset between the measured coordinates of the first reference position and the absolute coordinates of the first reference position, and determining a translation deviation of the robot based on the first coordinate offset; Determine a first measurement vector based on the measured coordinates of the first reference point and the measured coordinates of the second reference point, and determine a second measurement vector based on the measured coordinates of the first reference point and the measured coordinates of the third reference point; determining a first absolute vector based on the absolute coordinates of the first reference point and the absolute coordinates of the second reference point, and determining a second absolute vector based on the absolute coordinates of the first reference point and the absolute coordinates of the third reference point; Calculating a first plane rotation deviation based on a projection of the first measurement vector on the first plane and a first absolute vector; Calculating a second plane rotation deviation based on a projection of the first measurement vector on the second plane and the first absolute vector; Calculating a third plane rotation deviation based on a projection of the second measurement vector on the third plane and the second absolute vector; The position of the robot is calibrated based on the translation deviation, the first plane rotation deviation, the second plane rotation deviation and the third plane rotation deviation; after the position calibration is completed, the robot is used to collect data on the mold to be inspected placed on the mechanical turntable, and send the collected data to the control terminal device.

3. The intelligent mold quality detection method according to claim 1, characterized in that: The obtaining a target mold image from a mold database based on the first mold image includes: Performing initial feature extraction on the first mold image to obtain contour type features, size features, and pattern features; Obtaining a candidate mold image set from a mold database based on the contour type feature, size feature, and pattern feature; If the candidate mold image set includes a mold image, use the mold image as the target mold image; If the candidate mold image set includes at least two mold images, the similarity between the first mold image and each mold image in the candidate mold image set is calculated respectively, and the mold image with the highest similarity to the first mold image is used as the target mold image.

4. An intelligent mold quality detection method according to claim 1, characterized in that: The mold scanning control parameters include robot movement path, robot movement speed, laser scanning frequency and turntable rotation speed; The step of controlling the robot to scan the mold to be inspected on the mechanical turntable based on the mold scanning control parameters to obtain mold scanning data includes: controlling the rotation of the mechanical turntable based on the rotation speed of the turntable; Controlling the robot to scan the mold to be inspected on the mechanical turntable based on the robot scanning path, the robot moving speed, and the laser scanning frequency, to sequentially obtain three-dimensional point cloud data, first cross-sectional profile data, and second cross-sectional profile data; The three-dimensional point cloud data is a set of discrete three-dimensional coordinates of the entire surface of the mold, the first cross-sectional profile data is a circular profile of the transverse cross-section of the bottle mouth of the mold, and the second cross-sectional profile data is a continuous curve of the longitudinal symmetrical cross-section of the bottle body of the mold; the three-dimensional point cloud data, the first cross-sectional profile data, and the second cross-sectional profile data are used as the mold scanning data.

5. An intelligent mold quality detection method according to claim 4, characterized in that: The determining of the mold volume value and the mold three-dimensional contour data based on the mold scanning data includes: constructing a target coordinate system based on the first cross-sectional profile data and the second cross-sectional profile data; Generate a three-dimensional model of the mold based on the target coordinate system, the three-dimensional point cloud data, the first cross-sectional profile data, and the second cross-sectional profile data; A mold volume value and mold three-dimensional contour data are determined based on the mold three-dimensional model.

6. An intelligent mold quality detection method according to claim 5, characterized in that: The constructing of a target coordinate system based on the first cross-sectional profile data and the second cross-sectional profile data includes: Determine the center position of the bottle mouth based on the first cross-sectional profile data; and use the center position of the bottle mouth as the origin of the target coordinate system; The centerline of the bottle body is extracted based on the second cross-sectional profile data, where the upper endpoint of the centerline of the bottle body is the bottle mouth and the lower endpoint is the bottle bottom. The direction from the upper endpoint to the lower endpoint of the centerline of the bottle body is used as the positive direction of the Y axis of the target coordinate system; and the right direction perpendicular to the Y axis is used as the positive direction of the X axis of the target coordinate system.

7. An intelligent mold quality detection method according to claim 1, characterized in that: The determining of the mold quality inspection result based on the image data, the mold volume value, and the mold three-dimensional contour data includes: Obtaining template image data, standard volume value, and standard three-dimensional contour data corresponding to the target mold image; Calculating image similarity between the image data and the template image data; Calculating a volume difference between the mold volume value and the standard volume value; Calculating the three-dimensional profile similarity between the mold three-dimensional profile data and the standard three-dimensional profile data; A mold quality inspection result is determined based on the image similarity, the volume difference, and the three-dimensional contour similarity.

8. An intelligent mold quality detection system, characterized in that: Applicable to a control terminal device, the control terminal device is communicatively connected to a robot and a mechanical turntable respectively; the mechanical turntable is used to place a mold to be inspected, and the robot is used to collect data of the mold to be inspected placed on the mechanical turntable and send the collected data to the control terminal device; The control terminal device is used to control the working status of the robot and the mechanical turntable, and obtain the mold quality inspection result based on the data sent by the robot; The system comprises: a mold image recognition module configured to, in response to receiving a mold inspection instruction, control the robot to acquire a first mold image of the mold to be inspected on the mechanical turntable; and, in response to receiving the first mold image sent by the robot, obtain a target mold image and target mold information corresponding to the target mold image from a mold database based on the first mold image; the target mold image being an image that matches the first mold image, and the target mold information including mold scanning control parameters, mold type, and mold label information; a quality inspection module, configured to control the robot to scan the mold to be inspected on the mechanical turntable based on the mold scanning control parameters to obtain mold scanning data, and determine the mold volume value and the mold three-dimensional contour data based on the mold scanning data; A quality analysis module is configured to obtain image data of the mold based on the mold label information if the mold type is a glass mold containing an image, and determine a mold quality inspection result based on the image data, the mold volume value, and the mold three-dimensional contour data; and to determine a mold quality inspection result based on the mold volume value and the mold three-dimensional contour data if the mold type is a glass mold not containing an image.

9. A control terminal device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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