A high-precision positioning method and system for mold gate clamping

Abstract

The present application discloses a high-precision positioning method and system for mold gate clamping, which obtains first image information by performing image acquisition on the workpiece at the injection port; extracts features from the first image information using a convolution kernel to obtain workpiece structural features; classifies the workpiece at the injection port according to the workpiece structural features to determine the workpiece type; matches the gate type based on the workpiece type to obtain gate type information; analyzes the gate angle based on the workpiece structural features and gate type information to determine the gate angle; generates a position adjustment instruction based on the gate angle for sending to a position sensor controller to adjust the angle and position of a positioning module. This method solves the technical problem that the physical positioning method using only positioning plates for clamping is inherently not intelligent, and there is a positioning deviation caused by different shapes and sizes of injection molded workpieces that affects the processing effect. This method achieves the technical effect of intelligently adjusting the positioning module according to the size characteristics of the workpiece to ensure positioning accuracy.

Description

Technical Field

[0001] The present application relates to the technical field of gate clamping and positioning, and in particular to a high-precision positioning method and system for mold gate clamping. Background Art

[0002] Injection molding is a process of heating plastic to melt it, then injecting it into the cavity of a molding mold to form it, and then cooling it down and demolding it after the melt solidifies. In the process of injection molding, in addition to paying attention to the process control of each link of injection molding, it is also necessary to master the use of injection molds. Among them, the gate is an important component of the injection mold. The diversified gate types correspond to different injection molding types for different types of injection molding workpieces, which has a great impact on the final quality of injection molding. The gate clamping position needs to be accurately positioned to ensure the processing effect of the injection molding workpiece. The existing technology mainly uses EROWA positioning pieces for clamping and positioning, and there are positioning deviations caused by different shapes and sizes of different injection molding workpieces.

[0003] It is found that the above technology has at least the following technical problems:

[0004] In the prior art, only EROWA positioning sheets are used for physical positioning during clamping, and the inherent intelligence level of the method is not high. There is a technical problem that positioning deviation caused by different shapes and sizes of injection molded workpieces affects the processing effect. Summary of the invention

[0005] The purpose of this application is to provide a high-precision positioning method and system for mold gate clamping, in order to solve the technical problem that the existing technology only uses EROWA positioning pieces for physical positioning of clamping, the inherent intelligence level of the means is not high, and there are positioning deviations caused by different shapes and sizes of injection molded workpieces that affect the processing effect. It achieves the technical effect of intelligent analysis and matching of gate types according to the types of workpieces to be processed, meets the specific processing requirements of different workpieces, adjusts the positioning module according to the size characteristics of different workpieces, ensures the accuracy of positioning, and has a high degree of intelligence and a wide range of workpiece types.

[0006] In view of the above problems, the present application provides a high-precision positioning method and system for mold gate clamping.

[0007] In the first aspect, the present application provides a high-precision positioning method for mold gate clamping, and the method is applied to a mold gate clamping positioning system, the system including an image acquisition device and a position sensor controller, and the method includes: acquiring an image of a workpiece at a glue inlet by an image acquisition device to obtain first image information; extracting features from the first image information using a convolution kernel to obtain workpiece structural features; classifying the glue inlet workpiece according to the workpiece structural features to determine the workpiece type; matching the gate type based on the workpiece type to obtain the gate type information; analyzing the gate angle based on the workpiece structural features and the gate type information to determine the gate angle; generating a position adjustment instruction based on the gate angle, and the position adjustment instruction is used to send to the position sensor controller to adjust the angle and position of the positioning module.

[0008] On the other hand, the present application also provides a high-precision positioning system for mold gate clamping, which is used to perform a high-precision positioning method for mold gate clamping as described in the first aspect, and the system comprises:

[0009] A first acquisition unit, the first acquisition unit is used to acquire an image of the workpiece at the glue inlet through an image acquisition device to acquire first image information;

[0010] a second obtaining unit, the second obtaining unit being used to extract features from the first image information using a convolution kernel to obtain workpiece structural features;

[0011] A first determining unit, the first determining unit is used to classify the glue inlet workpiece according to the structural characteristics of the workpiece to determine the type of the workpiece;

[0012] A third obtaining unit, the third obtaining unit is used to match the gate type based on the workpiece type to obtain gate type information;

[0013] A second determining unit, the second determining unit is used to perform gate angle analysis based on the workpiece structural features and the gate type information to determine the gate angle;

[0014] A first execution unit is used to generate a position adjustment instruction according to the gate angle, and the position adjustment instruction is used to send to the position sensor controller to adjust the angle and position of the positioning module.

[0015] In a third aspect, the present application also provides a high-precision positioning system for mold gate clamping, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method described in the first aspect when executing the program.

[0016] In a fourth aspect, the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method described in any one of the first aspects is implemented.

[0017] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0018] The present application discloses a high-precision positioning method and system for mold gate clamping, wherein an image acquisition device is used to acquire an image of a workpiece at a glue inlet to obtain first image information; a convolution kernel is used to extract features from the first image information to obtain workpiece structural features; the glue inlet workpiece is classified according to the workpiece structural features to determine the workpiece type; a gate type is matched based on the workpiece type to obtain gate type information; a gate angle is analyzed based on the workpiece structural features and the gate type information to determine the gate angle; and a position adjustment instruction is generated according to the gate angle, and the position adjustment instruction is used to send to a position sensor controller to adjust the angle and position of a positioning module. The method achieves intelligent analysis and matching of gate types according to the type of workpiece to be processed, meets the specific processing requirements of different workpieces, adjusts the positioning module according to the size characteristics of different workpieces, ensures the accuracy of positioning, and has a high degree of intelligence and a wide range of workpiece types, thereby solving the technical problem that the existing technology only uses EROWA positioning sheets for physical positioning of clamping, the inherent degree of intelligence of the means is not high, and there are positioning deviations caused by different shapes and sizes of injection molded workpieces that affect the processing effect.

[0019] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0021] Figure 1 A schematic diagram of a process flow of a high-precision positioning method for mold gate clamping according to an embodiment of the present application;

[0022] Figure 2 This is a structural schematic diagram of a high-precision positioning system for mold gate clamping according to an embodiment of the present application;

[0023] Figure 3 It is a schematic diagram of the structure of an exemplary electronic device according to an embodiment of the present application.

[0024] Explanation of the reference numerals: first obtaining unit 11 , second obtaining unit 12 , first determining unit 13 , third obtaining unit 14 , second determining unit 15 , first executing unit 16 , bus 300 , receiver 301 , processor 302 , transmitter 303 , memory 304 , bus interface 305 . DETAILED DESCRIPTION

[0025] The embodiments of the present application provide a high-precision positioning method and system for mold gate clamping, thereby solving the technical problem that the prior art only uses EROWA positioning plates for physical positioning during clamping, the inherent intelligence level of the method is low, and there are positioning deviations caused by different shapes and sizes of injection molded workpieces that affect the processing effect.

[0026] Below, the technical solutions in the embodiments of the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0027] The overall idea of ​​the technical solution provided by this application is as follows:

[0028] The image acquisition device is used to acquire the image of the workpiece at the inlet to obtain the first image information; the convolution kernel is used to extract the features of the first image information to obtain the workpiece structure features; the workpiece at the inlet is classified according to the workpiece structure features to determine the workpiece type; the gate type is matched based on the workpiece type to obtain the gate type information; the gate angle is analyzed based on the workpiece structure features and the gate type information to determine the gate angle; according to the gate angle, a position adjustment instruction is generated, and the position adjustment instruction is used to send to the position sensor controller to adjust the angle and position of the positioning module. The intelligent analysis and matching of the gate type according to the type of the workpiece to be processed is achieved to meet the specific processing requirements of different workpieces, and the positioning module is adjusted according to the size features of different workpieces to ensure the accuracy of positioning, with a high degree of intelligence and a wide range of workpiece types.

[0029] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically described below in conjunction with the drawings in the specification.

[0030] Embodiment 1

[0031] Please see attached Figure 1 The embodiment of the present application provides a high-precision positioning method for mold gate clamping, the method is applied to a mold gate clamping positioning system, the system includes an image acquisition device, a position sensor controller, and the method includes:

[0032] Step S100: Capturing an image of the workpiece at the glue inlet by using an image acquisition device to obtain first image information;

[0033] Specifically, corresponding image acquisition and feature analysis are performed for different injection molded workpieces, laying the foundation for the selection and control of corresponding gate types for different workpiece shape and size characteristics, thereby ensuring the use effect of the gate mold. The image acquisition device is a high-definition camera that acquires images of the workpiece to be processed. The position sensor controller is mainly used to collect the position and angle of the gate and the position of the rubber head and to receive and control the adjustment control signal, which includes a position sensor and a controller, and the collected data is synchronously sent to the mold gate clamping and positioning system. The mold gate clamping and positioning system sends an adjustment instruction to the position sensor controller, and the position sensor controller controls the gate through the controller to adjust the instruction. The gate clamping and positioning device in the embodiment of the present application includes a positioning module and a fastening module. The positioning module includes a positioning plate, a rotatable base, and a position sensor. The rotatable base is set in the positioning of the positioning plate. When it is rotated into place, the rotating base is fixed by the fastening module, which can realize the rotation adjustment control of the gate clamping and positioning device, thereby achieving the effect of adjusting the gate processing angle.

[0034] Step S200: extracting features from the first image information using a convolution kernel to obtain workpiece structural features;

[0035] Furthermore, the method of using a convolution kernel to extract features from the first image information to obtain workpiece structural features includes: obtaining preset partition information; partitioning the first image information according to the preset partition information to obtain partition images; performing feature traversal analysis on the partition images to determine image features; marking workpiece edge identification points based on the image features, and connecting the marks to obtain workpiece edge information; determining workpiece shape features based on the workpiece edge information, and obtaining the workpiece structural features based on the workpiece shape features.

[0036] Specifically, the first image information contains the workpiece to be processed, and the structural features of the workpiece, such as shape, size, proportion, depth, thickness, angle, etc., are determined by extracting and analyzing the features of the image. The image information is partitioned using the convolution kernel, and the image features are identified for the partitions to determine the border of the workpiece. The changes in the color and light of the image are used to mark the changes, and the marked position points are connected to obtain the shape of the workpiece. The shape and structural features of the workpiece are obtained by analyzing the proportion and angle of the border shape. It should be understood that the convolution kernel is when an input image is given during image processing, and the pixels in a small area of ​​the input image are weighted averaged to become each corresponding pixel in the output image, where the weights are defined by a function, which is called the convolution kernel.

[0037] Step S300: classifying the glue inlet workpiece according to the workpiece structural features to determine the workpiece type;

[0038] Furthermore, the classification of the inlet workpiece according to the workpiece structural features includes: obtaining a historical workpiece feature set; obtaining a workpiece shape grade according to the historical workpiece feature set, and using the workpiece shape grade as a first node feature; determining a first-level workpiece feature set based on the workpiece shape grade, obtaining a contact surface angle grade, and using the contact surface angle grade as a second node feature; determining a second-level workpiece feature set based on the contact surface angle grade, obtaining a workpiece gate structure, and using the workpiece gate structure as a third node feature; constructing a workpiece gate feature classification model according to the first node feature, the second node feature, and the third node feature; inputting the workpiece structural features into the workpiece gate feature classification model to obtain workpiece gate structure information, and using the workpiece gate structure information as the workpiece type.

[0039] Furthermore, the constructing of the workpiece gate feature classification model according to the first node feature, the second node feature, and the third node feature includes: performing information theory coding operations on the first node feature, the second node feature, and the third node feature, respectively, to obtain first feature information entropy, second feature information entropy, and third feature information entropy; comparing the first feature information entropy, the second feature information entropy, and the third feature information entropy to determine multi-level classification nodes, and the multi-level classification nodes are sorted from small to large according to information entropy; based on the multi-level classification nodes and the historical workpiece feature set, the workpiece gate feature classification model is constructed.

[0040] Specifically, since different workpiece types are suitable for different gate types, workpiece structural features are used to classify the types and match the appropriate gate types. In order to improve the reliability of the classification results, the present application constructs a classification model for computer intelligent processing, uses historical workpiece feature sets for feature analysis, determines the features for workpiece type classification, classifies the dimensions of the workpieces, and classifies them according to the size. The level setting can be preset according to local processing links and conditions, such as including 5 levels, each level corresponds to a size information, and obtains the workpiece body grade; based on the workpiece body grade, a first-level workpiece feature set is determined, and the first-level workpiece feature set is extracted after classification according to the workpiece body grade, and corresponds to a feature set of different levels. Each workpiece body grade feature level corresponds to a feature set, and then The contact surface angle level is divided, which is affected by the plane structure of the workpiece. For example, some have a three-dimensional structure, or a cavity structure, etc., and the plane structure of the operating surface during gate processing is different. The angle level is divided according to the operating surface structure; based on the contact surface angle level, a secondary workpiece feature set is determined, which includes a workpiece feature set corresponding to the number of divided angle levels. On the basis of the secondary workpiece feature set, the workpiece gate structure features are classified. The workpiece gate structure features directly correspond to the gate type, such as a workpiece type with a large molded part volume and a deep contact surface, or a workpiece with a large volume and a small thickness. The final workpiece gate type is determined based on the body shape and the contact surface angle. When constructing the workpiece gate feature classification model, the features of each node are selected, determined and pruned. The features are selected by calculating the information entropy of each node feature. The larger the information entropy, the lower the feature purity and the less pure the classification. Classification can be based on the feature with the smallest information entropy to ensure the reliability of classification. The feature with the smallest information entropy is used as the first classification node. The optimal feature is selected from all training data in the first classification node, and the training data is divided into sub-feature sets. The classification of the sub-feature sets is continued, so that the final data is divided into a certain type, and the workpiece gate feature classification model is constructed. The workpiece gate feature classification model is optimized using a recursive method until it cannot be reduced, and the training convergence process of the workpiece gate feature classification model is completed.

[0041] Step S400: performing gate type matching based on the workpiece type to obtain gate type information;

[0042] Furthermore, the gate type matching based on the workpiece type, before obtaining the gate type information, includes: constructing a gate type table through big data, the gate type table including gate types and adaptable workpiece features, wherein the gate type corresponds to the adaptable workpiece features; obtaining a local gate type; matching the gate type table according to the local gate type to obtain a matching gate data table; obtaining local processing data; obtaining a processing type and a processing effect according to the local processing data; and updating the matching gate data table based on the local gate type, the processing type, and the processing effect.

[0043] Specifically, the workpiece type is matched with the gate type to find the gate that matches the type. Big data is used to search for basic data and find feature data corresponding to gate classification, including workpiece types and processing results corresponding to gate types. All relevant data collected by big data are used to extract parameters and establish a gate type table. The gate type table is the requirements and experience for dividing a wide spectrum of gate features. At the same time, it should also be optimized in combination with local gate types and processing conditions. Local gate types are all local gate types, such as rectangular gates, fan gates, film gates, point gates, etc. Local gate types are used to match in the constructed gate type table, find similar gate information, and form a matching gate data table. The processing effect is analyzed based on the processing data of local gate types, and the matching gate data table is updated using the analysis results of local processing data, so that the gate type table includes the workpiece characteristics and requirements corresponding to traditional gate types, and also includes local gate processing data, ensuring that more data results can be taken into account in the matching of gate types, so that various situations can be included in the continued parameter analysis, and the scope of adaptation is wider. The workpiece type is matched with the gate type in the matching gate data table to determine which gate to use, such as using a direct gate for large workpieces, a rectangular gate for small and deep workpieces, and a fan gate for large, flat and thin workpieces. The determined workpiece type is matched with the local gate type in the matching gate data table to determine the matching gate type.

[0044] Step S500: performing gate angle analysis based on the workpiece structural features and the gate type information to determine the gate angle;

[0045] Step S600: generating a position adjustment instruction according to the gate angle, wherein the position adjustment instruction is sent to a position sensor controller to adjust the angle and position of the positioning module.

[0046] Specifically, a specific gate parameter analysis is performed according to the determined workpiece structural features and the matched gate type information. The gate type information is the matched local gate type. The specific set position and parameters of the local gate type are used to match the parameters with the currently analyzed workpiece type. The current gate mold position and angle are monitored by the position sensor function of the position sensor controller, and the gate processing requirements of the incoming workpiece and the current mold position and angle are matched and analyzed. If the match is successful, the execution instruction is obtained and processed according to the current gate type and position. If it is unsuccessful, the adjustment information is sent to adjust the gate type, angle, and position to meet the requirements of the current processing workpiece. When the adjustment is completed, the adjusted position is monitored by the position sensor controller. When it is correct, the rotating base under the positioning module is locked by the locking module, and then processing is performed to ensure the processing position in the gate, so that the gate is at a vertical angle to the workpiece processing surface, to ensure the processing effect, and to ensure the processing quality of the product. It achieves intelligent analysis and matching of gate types according to the types of workpieces to meet the specific processing requirements of different workpieces, adjusts the positioning module according to the dimensional characteristics of different workpieces, ensures the accuracy of positioning, and has a high degree of intelligence and a wide range of workpiece types. It solves the technical problem that the existing technology only uses EROWA positioning pieces for physical positioning during clamping, the inherent intelligence level of the means is not high, and there is a positioning deviation caused by different shapes and sizes of injection molded workpieces that affects the processing effect.

[0047] Furthermore, the method also includes: acquiring a workpiece entry image through an image acquisition device; acquiring a workpiece prediction angle based on the workpiece entry image; acquiring a positioning module position information through a position sensor controller; acquiring a workpiece positioning angle based on the workpiece prediction angle and the positioning module position information; judging whether a gate angle meets a preset requirement based on the workpiece positioning angle, wherein the preset requirement is that the gate is at a vertical angle to the workpiece machining surface; when not met, obtaining an angle adjustment instruction based on the workpiece positioning angle.

[0048] Specifically, after the adjustment instruction is determined, the workpiece being processed is continuously monitored to ensure that the entry angle and the adjustment position match, so as to avoid the deviation of the gate position caused by the error in the angle control of the workpiece when entering. The workpiece to be entered is monitored in real time through the image acquisition device, and the image information is collected to locate the position of the entire entry process. The frame position of the entering workpiece is marked by image acquisition to determine whether the workpiece has an entry angle deviation. The workpiece entry image is the image collected when the workpiece enters the gate. By performing image recognition and feature extraction on the workpiece entry image, the workpiece frame is marked to predict the angle between the workpiece and the positioning module, that is, the workpiece predicted angle. The position of the positioning module is collected by the position sensor controller. Based on the workpiece predicted angle and the positioning module position information, it is determined whether it can ensure that the gate and the workpiece processing surface are at a vertical angle. If it can, continue the current position. If there is a deviation, the angle of the workpiece positioning module or the gate position can be fine-tuned by generating corresponding adjustment instructions. The angle adjustment instruction is adjusted according to the workpiece predicted angle and the angle actually monitored by the positioning module to determine that the angle between the workpiece and the gate is vertical, so as to ensure the processing effect and achieve precise control of the position in the gate processing.

[0049] Furthermore, the method also includes: determining a processing gate angle according to the angle adjustment instruction; obtaining a gate processing result; forming first processing data based on the processing gate angle, the gate processing result, and the gate type; adding the first processing data to the local processing data, using the updated local processing data to verify the matching gate data table, and updating the matching gate data table based on the verification result.

[0050] Specifically, during the mold gate clamping process, the process of adjusting and processing the workpiece, positioning module, gate position, etc. is recorded, the data in the matching gate data table is verified and updated, and the processing results are tested through the adjusted parameters to obtain the gate processing effect of the processed workpiece, and the first processing data is formed. The first processing data is the processing gate angle, the final processing result of the workpiece, the type of gate used, the workpiece angle, the gate angle, the angle of the positioning module, etc. These working data in actual operations correspond to one processing data for one processing. The first processing data is a record of any processing data, which supplements and improves the local processing data. Each processing data is synchronized to the local processing data for storage, which is convenient for subsequent analysis and research, and continuously improves the accuracy of positioning. At the same time, according to the first processing data, the matching gate data table established is matched according to the gate type, parameters, and results to determine whether there is any abnormality. If there is an abnormality in the matching gate data table, the processing data is analyzed and processed to determine the content in the matching gate data table, and the matching gate data table is updated. If there is no abnormality, it is determined whether the matching gate data table records this type of data. If it has been recorded, it is not processed. If not recorded, the parameters of the first processing data are extracted. In order to make the parameter content correspond to the parameter content in the matching gate data table, the corresponding extracted data is stored in the matching gate data table, so as to realize the continuous improvement and updating of the matching gate data table based on the actual processing data, ensure the reliability of the data corresponding to the gate type, provide guarantee for the effective determination of the gate parameters, and at the same time improve the technical effect of the positioning accuracy of the mold gate clamping.

[0051] Embodiment 2

[0052] Based on the high-precision positioning method for mold gate clamping in the aforementioned embodiment, the present invention also provides a high-precision positioning system for mold gate clamping, see the attached Figure 2 , the system comprising:

[0053] A first obtaining unit 11, wherein the first obtaining unit 11 is used to obtain an image of the workpiece at the glue inlet through an image acquisition device to obtain first image information;

[0054] A second obtaining unit 12, the second obtaining unit 12 is used to extract features from the first image information using a convolution kernel to obtain workpiece structural features;

[0055] A first determining unit 13, the first determining unit 13 is used to classify the glue inlet workpiece according to the structural characteristics of the workpiece to determine the type of the workpiece;

[0056] A third obtaining unit 14, the third obtaining unit 14 is used to match the gate type based on the workpiece type to obtain gate type information;

[0057] A second determining unit 15, the second determining unit 15 is used to perform gate angle analysis based on the workpiece structural features and the gate type information to determine the gate angle;

[0058] The first execution unit 16 is used to generate a position adjustment instruction according to the gate angle, and the position adjustment instruction is used to send to the position sensor controller to adjust the angle and position of the positioning module.

[0059] Furthermore, the system also includes:

[0060] A fourth obtaining unit, the fourth obtaining unit is used to obtain preset partition information;

[0061] a fifth obtaining unit, configured to partition the first image information according to the preset partition information to obtain a partition image;

[0062] a third determining unit, the third determining unit being used to perform feature traversal analysis on the partition image to determine image features;

[0063] A sixth obtaining unit, the sixth obtaining unit is used to mark the workpiece edge identification points based on the image features, and connect the marks to obtain the workpiece edge information;

[0064] A seventh obtaining unit, the seventh obtaining unit is used to determine a shape feature of the workpiece based on the workpiece edge information, and obtain the workpiece structural feature according to the workpiece shape feature.

[0065] Furthermore, the system also includes:

[0066] An eighth obtaining unit, the eighth obtaining unit being used to obtain a historical artifact feature set;

[0067] A second execution unit, the second execution unit is used to obtain a workpiece body type level according to the historical workpiece feature set, and use the workpiece body type level as a first node feature;

[0068] A third execution unit, the third execution unit is used to determine a primary workpiece feature set based on the workpiece body grade, obtain a contact surface angle grade, and use the contact surface angle grade as a second node feature;

[0069] a fourth execution unit, the fourth execution unit being used to determine a secondary workpiece feature set based on the contact surface angle level, obtain a workpiece gate structure, and use the workpiece gate structure as a third node feature;

[0070] A first construction unit, the first construction unit is used to construct a workpiece gate feature classification model according to the first node feature, the second node feature, and the third node feature;

[0071] A fifth execution unit is used to input the workpiece structural feature into the workpiece gate feature classification model to obtain workpiece gate structure information, and use the workpiece gate structure information as the workpiece type.

[0072] Furthermore, the system also includes:

[0073] A ninth obtaining unit, the ninth obtaining unit is used to perform information theory coding operations on the first node feature, the second node feature, and the third node feature, respectively, to obtain first feature information entropy, second feature information entropy, and third feature information entropy;

[0074] A sixth execution unit, the sixth execution unit is used to compare the first feature information entropy, the second feature information entropy, and the third feature information entropy to determine a multi-level classification node, and the multi-level classification node is sorted from small to large according to the information entropy;

[0075] A second construction unit is used to construct the workpiece gate feature classification model based on the multi-level classification nodes and the historical workpiece feature set.

[0076] Furthermore, the system also includes:

[0077] A third construction unit, the third construction unit is used to construct a gate type table based on big data, the gate type table includes a gate type and an adapted workpiece feature, wherein the gate type corresponds to the adapted workpiece feature;

[0078] a tenth obtaining unit, the tenth obtaining unit being used to obtain a local gate type;

[0079] an eleventh obtaining unit, the eleventh obtaining unit being used to match the local gate type in the gate type table to obtain a matching gate data table;

[0080] a twelfth obtaining unit, the twelfth obtaining unit being used to obtain local processing data;

[0081] a thirteenth obtaining unit, the thirteenth obtaining unit being used to obtain a processing type and a processing effect according to the local processing data;

[0082] A first updating unit, wherein the first updating unit is used to update the matching gate data table based on the local gate type, the processing type, and the processing effect.

[0083] Furthermore, the system also includes:

[0084] A fourteenth obtaining unit, the fourteenth obtaining unit is used to obtain an image of the workpiece entering through an image acquisition device;

[0085] a fifteenth obtaining unit, the fifteenth obtaining unit being used to obtain a workpiece prediction angle according to the workpiece entry image;

[0086] A sixteenth obtaining unit, the sixteenth obtaining unit is used to obtain the position information of the positioning module through the position sensor controller;

[0087] A seventeenth obtaining unit, the seventeenth obtaining unit is used to obtain a workpiece positioning angle based on the workpiece prediction angle and the positioning module position information;

[0088] A first judgment unit, the first judgment unit is used to judge whether the gate angle meets a preset requirement based on the workpiece positioning angle, wherein the preset requirement is that the gate is at a vertical angle to the workpiece processing surface;

[0089] An eighteenth obtaining unit is used to obtain an angle adjustment instruction based on the workpiece positioning angle when the condition is not satisfied.

[0090] Furthermore, the system also includes:

[0091] a fourth determining unit, the fourth determining unit being used to determine a processing gate angle according to the angle adjustment instruction;

[0092] A nineteenth obtaining unit, the nineteenth obtaining unit is used to obtain a gate processing result;

[0093] A first assembly unit, the first assembly unit is used to assemble first processing data based on the processing gate angle, the gate processing result, and the gate type;

[0094] A seventh execution unit, wherein the seventh execution unit is used to add the first processing data to the local processing data, verify the matching gate data table using the updated local processing data, and update the matching gate data table based on the verification result.

[0095] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences between the expected embodiments. Figure 1The high-precision positioning method and specific examples of a mold gate clamp in the first embodiment are also applicable to a high-precision positioning system for a mold gate clamp in this embodiment. Through the above detailed description of the high-precision positioning method for a mold gate clamp, those skilled in the art can clearly know the high-precision positioning system for a mold gate clamp in this embodiment, so for the sake of brevity of the specification, it will not be described in detail here. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0096] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0097] Exemplary Electronic Devices

[0098] Reference below Figure 3 To describe the electronic device of the embodiment of the present application.

[0099] Figure 3 The figure shows a schematic structural diagram of an electronic device according to an embodiment of the present application.

[0100] Based on the inventive concept of a high-precision positioning method for mold gate clamping in the aforementioned embodiment, the present invention also provides a high-precision positioning system for mold gate clamping, on which a computer program is stored, and when the program is executed by a processor, the steps of any method of the high-precision positioning method for mold gate clamping described above are implemented.

[0101] Among them, Figure 3 In the embodiment of the present invention, a bus architecture (represented by bus 300) is shown, which may include any number of interconnected buses and bridges, and bus 300 links various circuits including one or more processors represented by processor 302 and memory represented by memory 304. Bus 300 may also link various other circuits such as peripherals, voltage regulators, and power management circuits, which are well known in the art and are not further described herein. Bus interface 305 provides an interface between bus 300 and receiver 301 and transmitter 303. Receiver 301 and transmitter 303 may be the same element, namely a transceiver, which provides a unit for communicating with various other devices over a transmission medium.

[0102] The processor 302 is responsible for managing the bus 300 and general processing, while the memory 304 may be used to store data used by the processor 302 when performing operations.

[0103] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, devices, or computer program products. Therefore, the present application may adopt a complete software embodiment, a complete hardware embodiment, or a combination of software and hardware embodiments. In addition, the present application is in the form of a computer program product that can be implemented on one or more computer-usable storage media containing computer-usable program codes. The computer-usable storage medium includes, but is not limited to, various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), disk storage, compact disc read-only memories (CD-ROM), optical memories, etc.

[0104] The present invention is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products of the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A system that specifies the functions of a box or multiple boxes.

[0105] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction system, which is implemented in the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0106] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they understand the basic creative concepts.

[0107] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention belong to the scope of the same technology of the present invention, the present invention is also intended to include these modifications and variations.

Claims

1. A high-precision positioning method for mold gate clamping, characterized in that: The method is applied to a mold gate clamping and positioning system, the system includes an image acquisition device and a position sensor controller, and the method includes: The image acquisition device is used to acquire an image of the workpiece at the glue inlet to obtain first image information; Using a convolution kernel to extract features from the first image information to obtain workpiece structural features; Classifying the workpiece at the glue inlet according to the structural characteristics of the workpiece to determine the type of the workpiece; Perform gate type matching based on the workpiece type to obtain gate type information; Perform gate angle analysis based on the workpiece structural features and the gate type information to determine the gate angle; Generate a position adjustment instruction according to the gate angle, wherein the position adjustment instruction is used to be sent to the position sensor controller to adjust the angle and position of the positioning module; Wherein, the method for adjusting the angle and position of the positioning module according to the position sensor controller includes: Acquire the workpiece entry image by using an image acquisition device; Obtaining a predicted angle of the workpiece according to the workpiece entry image; The position information of the positioning module is acquired through the position sensor controller; Based on the workpiece prediction angle and the positioning module position information, obtaining the workpiece positioning angle; Based on the workpiece positioning angle, judging whether the gate angle meets the preset requirement, wherein the preset requirement is that the gate is at a vertical angle to the workpiece processing surface; When it is not satisfied, an angle adjustment instruction is obtained based on the workpiece positioning angle.

2. The method according to claim 1, characterized in that The step of extracting features from the first image information using a convolution kernel to obtain workpiece structural features includes: Get the preset partition information; Partition the first image information according to the preset partition information to obtain a partition image; Performing feature traversal analysis on the partitioned image to determine image features; Marking workpiece edge identification points based on the image features, and connecting the marks to obtain workpiece edge information; Based on the workpiece edge information, the workpiece shape feature is determined, and according to the workpiece shape feature, the workpiece structural feature is obtained.

3. The method according to claim 1, characterized in that The method of classifying the glue inlet workpieces according to the structural features of the workpieces includes: Obtaining a historical artifact feature set; According to the historical workpiece feature set, a workpiece body grade is obtained, and the workpiece body grade is used as a first node feature; Determine a primary workpiece feature set based on the workpiece body grade, obtain a contact surface angle grade, and use the contact surface angle grade as a second node feature; Determine a secondary workpiece feature set based on the contact surface angle level, obtain a workpiece gate structure, and use the workpiece gate structure as a third node feature; Constructing a workpiece gate feature classification model according to the first node feature, the second node feature, and the third node feature; The workpiece structure feature is input into the workpiece gate feature classification model to obtain workpiece gate structure information, and the workpiece gate structure information is used as the workpiece type.

4. The method according to claim 3, characterized in that The step of constructing a workpiece gate feature classification model according to the first node feature, the second node feature, and the third node feature includes: Performing information theory coding operations on the first node feature, the second node feature, and the third node feature to obtain first feature information entropy, second feature information entropy, and third feature information entropy; Compare the first feature information entropy, the second feature information entropy, and the third feature information entropy to determine a multi-level classification node, and sort the multi-level classification nodes from small to large according to the information entropy; Based on the multi-level classification nodes and the historical workpiece feature set, the workpiece gate feature classification model is constructed.

5. The method according to claim 1, characterized in that Before the gate type matching based on the workpiece type and obtaining the gate type information, the method includes: Constructing a gate type table through big data, wherein the gate type table includes gate types and adapted workpiece features, wherein the gate type corresponds to the adapted workpiece features; Get the local gate type; Matching the local gate type in the gate type table to obtain a matching gate data table; Obtain local processing data; Obtaining a processing type and a processing effect according to the local processing data; Based on the local gate type, the processing type, and the processing effect, the matching gate data table is updated.

6. The method according to claim 5, characterized in that The method further comprises: Determine the processing gate angle according to the angle adjustment instruction; Get gate treatment results; Based on the processing gate angle, the gate processing result, and the gate type, forming first processing data; The first processing data is added to the local processing data, the matching gate data table is verified using the updated local processing data, and the matching gate data table is updated based on the verification result.

7. A high-precision positioning system for mold gate clamping, characterized in that: The system comprises: A first acquisition unit, the first acquisition unit is used to acquire an image of the workpiece at the glue inlet through an image acquisition device to acquire first image information; a second obtaining unit, the second obtaining unit being used to extract features from the first image information using a convolution kernel to obtain workpiece structural features; A first determining unit, the first determining unit is used to classify the glue inlet workpiece according to the structural characteristics of the workpiece to determine the type of the workpiece; A third obtaining unit, the third obtaining unit is used to match the gate type based on the workpiece type to obtain gate type information; A second determining unit, the second determining unit is used to perform gate angle analysis based on the workpiece structural features and the gate type information to determine the gate angle; A first execution unit, the first execution unit is used to generate a position adjustment instruction according to the gate angle, and the position adjustment instruction is used to send to the position sensor controller to adjust the angle and position of the positioning module; A fourteenth obtaining unit, the fourteenth obtaining unit is used to obtain an image of the workpiece entering through an image acquisition device; a fifteenth obtaining unit, the fifteenth obtaining unit being used to obtain a workpiece prediction angle according to the workpiece entry image; A sixteenth obtaining unit, the sixteenth obtaining unit is used to obtain the position information of the positioning module through the position sensor controller; A seventeenth obtaining unit, the seventeenth obtaining unit is used to obtain a workpiece positioning angle based on the workpiece prediction angle and the positioning module position information; A first judgment unit, the first judgment unit is used to judge whether the gate angle meets a preset requirement based on the workpiece positioning angle, wherein the preset requirement is that the gate is at a vertical angle to the workpiece processing surface; An eighteenth obtaining unit is used to obtain an angle adjustment instruction based on the workpiece positioning angle when the condition is not satisfied.

8. A high-precision positioning system for mold gate clamping, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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