A method and system for analyzing the forming quality of a rubber glove

Abstract

The application discloses a forming quality analysis method and system of rubber gloves, and belongs to the technical field of artificial intelligence, which comprises the following steps: acquiring the use of a preset rubber glove and matching a standard; acquiring a video of a finished glove by using an image acquisition device component, and obtaining a preset finished product integrity result; calling a preset objective standard and a preset subjective standard to analyze the quality of the preset rubber glove, and obtaining a preset analysis result; acquiring and traversing a preset factor index to obtain a preset factor index parameter; analyzing the preset factor index parameter to obtain a preset weight coefficient of the preset factor index, and combining the preset factor index parameter to obtain a preset quality index through weighted calculation; and matching a preset quality grade based on the preset quality index, so as to analyze the quality of the preset rubber glove. The application solves the technical problems of low detection efficiency and low quality analysis accuracy in the prior art, and achieves the technical effects of improving quality inspection efficiency and improving quality analysis accuracy.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence technology, and in particular to a method and system for analyzing the molding quality of rubber gloves. Background Technology

[0002] Rubber gloves are an important type of protective equipment widely used in medical, laboratory, and food processing fields. However, defects such as breaks, tears, and uneven thickness can occur during the manufacturing process, potentially causing injury or even endangering life. Therefore, accurate analysis and evaluation of the molding quality of rubber gloves are crucial.

[0003] Currently, the analysis of the molding quality of rubber gloves mainly relies on manual quality inspection, such as using air inflation testing or sampling water filling testing. These methods are not only inefficient but also prone to misjudgment during the analysis process, thus affecting the accuracy of the quality analysis. Therefore, existing rubber glove molding quality analysis technologies suffer from problems of low quality inspection efficiency and low accuracy. Summary of the Invention

[0004] This application provides a method and system for analyzing the molding quality of rubber gloves, which solves the technical problems of low quality inspection efficiency and low quality analysis accuracy in the prior art, and achieves the technical effect of improving quality inspection efficiency and improving quality analysis accuracy.

[0005] In a first aspect, embodiments of this application provide a method for analyzing the molding quality of rubber gloves. The method includes: obtaining a preset intended use of the rubber glove and matching it with preset glove standards, wherein the preset glove standards include preset subjective standards and preset objective standards; and using an image acquisition device to capture video of the finished product of the preset rubber glove, obtaining a preset finished product video, and analyzing the preset finished product video to obtain a preset finished product integrity result; when the preset finished product integrity result indicates that the finished product is complete, calling the preset objective standards to perform quality analysis on the preset rubber glove, obtaining a preset objective index analysis result; when the preset objective index analysis... When the objective indicators are qualified, the preset subjective standards are invoked to perform quality analysis on the preset rubber gloves, and the preset subjective indicator analysis results are obtained; preset factor indicators are obtained, and the preset factor indicators are traversed in the preset subjective indicator analysis results to obtain preset factor indicator parameters, wherein the preset factor indicator parameters and the preset factor indicators have a corresponding relationship; the preset factor indicator parameters are analyzed to obtain the preset weight coefficients of the preset factor indicators, and the preset quality index is calculated by weighting the preset factor indicator parameters; the preset quality index is matched with a preset quality level, wherein the preset quality level is used to perform quality analysis on the preset rubber gloves.

[0006] On the other hand, this application embodiment also provides a molding quality analysis system for rubber gloves, wherein the molding quality analysis system includes: a preset data acquisition module, which is used to acquire the preset glove's preset glove purpose and match it with preset glove standards, wherein the preset glove standards include preset subjective standards and preset objective standards; an image acquisition and analysis module, which uses an image acquisition device component to acquire video of the finished product of the preset rubber glove, obtain a preset finished product video, and analyze the preset finished product video to obtain a preset finished product integrity result; an objective standard analysis module, which is used to call the preset objective standards to perform quality analysis on the preset rubber glove when the preset finished product integrity result is that the finished product is complete, and obtain preset objective index analysis results; and a subjective standard analysis module, which is used to perform quality analysis on the preset rubber glove when the preset finished product integrity result is that the finished product is complete, and to ... complete, and to obtain preset objective index analysis results; and a subjective standard analysis module, which is used to perform quality analysis on the preset rubber glove when the preset finished product integrity result is complete, and to obtain preset objective index analysis results; and a subjective standard analysis module, which is used to perform quality analysis on the preset finished product when the preset finished product integrity result is complete, and to obtain preset objective index analysis results. When the preset objective indicator analysis result is qualified, the preset subjective standard is invoked to perform quality analysis on the preset rubber gloves, obtaining the preset subjective indicator analysis result; a preset factor indicator parameter module is used to obtain preset factor indicators and iterate through the preset subjective indicator analysis result to obtain preset factor indicator parameters, wherein the preset factor indicator parameters and the preset factor indicators have a corresponding relationship; a preset quality index module is used to analyze the preset factor indicator parameters to obtain the preset weight coefficients of the preset factor indicators, and calculate the preset quality index by weighting the preset factor indicator parameters; a preset quality level module is used to match a preset quality level based on the preset quality index, wherein the preset quality level is used to perform quality analysis on the preset rubber gloves.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages: 1. By employing an algorithmic detection method using image acquisition equipment components and image processing technology to inspect finished gloves, the technical problem of low quality inspection efficiency in existing technologies is effectively solved, thus achieving the technical effect of improving quality inspection efficiency.

[0008] 2. By employing different algorithms to accurately determine indicator parameters and progressively detect the integrity, objective indicators, and subjective indicators of the finished glove, the technical problem of low accuracy in quality inspection in existing technologies is effectively solved, thus achieving the technical effect of improving the accuracy of quality analysis. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0010] Figure 1 A flowchart illustrating a method for analyzing the molding quality of rubber gloves provided in this application embodiment; Figure 2 A schematic diagram of the process for obtaining target key image frames in a method for analyzing the molding quality of a rubber glove provided in an embodiment of this application; Figure 3 A schematic flowchart illustrating the process of obtaining preset factor indicators in a method for analyzing the molding quality of a rubber glove provided in this application embodiment; Figure 4 This is a schematic diagram of the structure of a molding quality analysis system for rubber gloves provided in an embodiment of this application.

[0011] Explanation of reference numerals in the attached figures: Preset data acquisition module 11; Image acquisition and analysis module 12; Objective standard analysis module 13; Subjective standard analysis module 14; Preset factor index parameter module 15; Preset quality index module 16; Preset quality level module 17. Detailed Implementation

[0012] This application provides a method and system for analyzing the molding quality of rubber gloves, which solves the technical problems of low efficiency and low accuracy in quality inspection in the prior art, and achieves the technical effect of improving quality inspection efficiency and accuracy.

[0013] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. It should also be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all of them.

[0014] Example 1

[0015] like Figure 1 As shown, this application provides a method for analyzing the molding quality of rubber gloves, the method comprising: Step S100: Obtain the intended use of the pre-defined rubber gloves and match it with the pre-defined glove standards. The pre-defined glove standards include pre-defined subjective standards and pre-defined objective standards. Specifically, the intended use of the pre-defined rubber gloves can be obtained through product manuals, industry standards, product promotional images, customer-specific requirements, etc., and the required standards for the rubber gloves, such as waterproofness, thickness, color, chemical resistance, insulation, flexibility, feel, comfort, and color perception, are determined as the pre-defined glove standards.

[0016] Among them, pre-set subjective standards refer to standards that cannot be measured by actual means and are based on human subjective experience and evaluation, such as feel, comfort, flexibility, and color perception. Pre-set objective standards refer to evaluation indicators of physical or chemical properties that can be achieved through scientific testing and mathematical statistical analysis, such as glove thickness, stitch tightness, and size.

[0017] By establishing pre-defined glove standards, product and standard requirements can be clearly defined, which facilitates the development of reasonable production and testing plans, improves product quality and performance, and reduces quality risks.

[0018] Step S200: Use the image acquisition device component to acquire video of the finished product of the preset rubber glove, obtain the video of the preset finished product, and analyze the video of the preset finished product to obtain the integrity result of the preset finished product; Specifically, image acquisition equipment components are built into the product line based on the characteristics of the products. For example, rubber gloves used in the pharmaceutical industry have high standards. Image acquisition devices can be mounted at different distances in different directions such as front and back, left and right, up and down, upper left, lower left, upper right, and lower right of the rubber gloves. These image acquisition devices together form an image acquisition equipment component to obtain detailed information about the gloves.

[0019] Next, video analysis technology is used to process the video, extracting glove image frames from different angles and distances of the same glove. Image registration technology is used to register the images from different angles and integrate them into a whole image of the glove under inspection. Image processing technology is used to combine the whole images of each glove under inspection into a preset finished video. Finally, the video is sent to the system for analysis.

[0020] After receiving the preset finished product video, the system checks the integrity of the gloves. For example, it uses an edge detection algorithm to detect and segment the edges of the gloves, check whether the edges are intact, and whether there are cracks, wounds or other defects, and generates an evaluation result. It also uses a texture analysis algorithm to analyze the texture and patterns on the surface of the gloves to detect whether the surface of the gloves is flat and generates an evaluation result.

[0021] Finished gloves are evaluated using different algorithms according to various integrity standards, and evaluation results are generated to determine whether the gloves are considered complete. If the gloves are complete, they proceed to the next step; otherwise, they are removed from the production line. Image processing analysis and edge detection technologies are used for product quality analysis, which significantly improves inspection efficiency and enhances the accuracy of analysis compared to manual inspection in existing technologies.

[0022] Step S300: When the preset finished product integrity result is that the finished product is complete, the preset objective standard is called to perform quality analysis on the preset rubber gloves to obtain the preset objective index analysis result; Specifically, when the preset finished product integrity result is "finished product complete," it means that the overall appearance of the rubber gloves has no obvious defects, tears, or damage, and meets the preset finished product integrity standard. At this time, preset objective standards are invoked to perform quality analysis on the rubber gloves to obtain the preset objective index analysis results.

[0023] First, confirm the pre-defined objective standards used, including standards for appearance, physical properties, chemical properties, manufacturing processes, and safety and hygiene, to ensure the accuracy and reliability of the analytical results. Second, select a certain number of rubber glove samples from the production line or warehouse according to the requirements of the pre-defined objective standards, ensuring the representativeness and randomness of the samples. Then, conduct necessary tests and inspections on the samples according to the requirements of the pre-defined objective standards to obtain data on various quality parameters and performance indicators, such as tensile force, breaking strength, pH, insulation, and light transmittance. Finally, based on the test results, evaluate and integrate the quality performance of the rubber gloves in various aspects to generate the pre-defined objective index analysis results. By conducting quality analysis based on pre-defined objective standards, the quality level of the rubber gloves can be further monitored, improving the accuracy of the quality analysis.

[0024] Step S400: When the preset objective index analysis result is that the objective index is qualified, the preset subjective standard is called to perform quality analysis on the preset rubber glove to obtain the preset subjective index analysis result; Specifically, a satisfactory result from the pre-set objective indicator analysis means that the rubber gloves meet the pre-set objective standards, thus providing a certain level of quality assurance. However, further in-depth quality analysis is still needed to improve the accuracy of the rubber gloves' quality inspection. Pre-set subjective standards refer to standards that are more subjective and personal than objective standards. They are usually based on the experience, feelings, and expectations of users and manufacturers, rather than on measurement results. Examples include feel, softness, comfort, and personal preferences.

[0025] First, determine the preset subjective standards to be used, such as requirements for feel, softness, comfort, and personal preference. Second, select a certain number of rubber glove samples based on the preset subjective standards. Then, conduct practical use tests on the selected rubber glove samples to evaluate indicators such as feel, softness, comfort, and appropriate capacity. Finally, based on the results of the practical use tests, evaluate the performance of the preset rubber gloves according to the preset subjective standards, and generate the subjective indicator analysis results.

[0026] By applying preset subjective standards to perform quality analysis on preset rubber gloves, a more comprehensive and in-depth understanding of user needs and satisfaction can be achieved, determining whether the rubber gloves are sufficiently comfortable, user-friendly, and easy to use. Conducting integrity assessments, objective standard assessments, and subjective standard assessments of the rubber gloves, analyzing their quality from different dimensions, significantly reduces the misjudgment rate of rubber glove quality and improves the accuracy of quality inspection.

[0027] Step S500: Obtain preset factor indicators, and iterate through the preset subjective indicator analysis results to obtain preset factor indicator parameters, wherein the preset factor indicator parameters have a corresponding relationship with the preset factor indicators; Specifically, the preset factor indicators refer to the subjective factor indicators that have a significant impact on the quality of finished products, determined through mathematical analysis techniques by performing correlation analysis between the set of subjective factor indicators constructed based on preset subjective standards and the finished product quality index in the historical glove quality analysis results. The preset factor indicators exclude those preset subjective indicators that have a low correlation with the quality of finished products.

[0028] Using pre-defined factor indicators as independent variables and pre-defined subjective indicators as dependent variables, linear and nonlinear models are established using multiple linear regression analysis. Model parameters are determined by estimating the slope and intercept using the least squares method, generating parameters for the pre-defined factor indicators, which correspond to the pre-defined factor indicators. By determining the parameters of the pre-defined factor indicators, the degree of influence of the pre-defined factor indicators on the subjective indicators is obtained, thus objectifying and refining the subjective indicators, improving the quality analysis standards for finished gloves, and ultimately enhancing the accuracy of quality analysis.

[0029] Step S600: Analyze the preset factor index parameters to obtain the preset weight coefficients of the preset factor indexes, and calculate the preset quality index by weighting the preset factor index parameters; Specifically, when analyzing preset factor indicator parameters to obtain preset weight coefficients, the Analytic Hierarchy Process (AHP) can be used. First, define the evaluation objectives and standards, categorizing indicator parameters into those with and without influence. Second, construct a hierarchical structure, decomposing the evaluation objectives and standards into several factor levels, forming a tree-like hierarchical structure, which can be visualized using charts. Then, set a weight comparison matrix, combining relevant personnel's experience and questionnaires to determine a fuzzy matrix, i.e., constructing an n*n matrix based on the weight values ​​obtained after pairwise comparisons of each factor, where n is the factor level. Finally, analyze the matrix to obtain the weight coefficients at each level, calculate the eigenvector of the matrix, and after normalization, only non-zero elements are allowed. Use the obtained eigenvector as the hierarchical weight coefficient to obtain the preset weight coefficients for each indicator. Finally, calculate the preset quality index by weighting the preset factor indicator parameters.

[0030] By setting a quality index, the overall quality level of rubber gloves can be comprehensively evaluated, and the impact of various factors on the quality of rubber gloves can be compared and analyzed, thereby improving the accuracy of glove quality analysis.

[0031] Step S700: Match a preset quality level based on the preset quality index, wherein the preset quality level is used to perform quality analysis on the preset rubber gloves.

[0032] Specifically, first, a preset quality level is determined, such as excellent, good, medium, and poor quality. Then, based on product requirements, the finished product quality is divided into multiple quality indices. These quality indices are measured, and the data are normalized and statistically analyzed. Finally, the quality level is determined based on the statistically analyzed quality indices, thus matching the quality indices with the quality levels. By using preset quality levels instead of preset quality indices to define the quality of preset rubber gloves, complex numerical indicators are normalized and simplified, thereby enabling efficient quality analysis and improving testing efficiency.

[0033] Furthermore, embodiments of this application also include: Step S210: The image acquisition device assembly includes P image acquisition devices, where P is an integer greater than or equal to 1.

[0034] Specifically, in the device for acquiring images of the finished rubber gloves, an image acquisition device component is installed according to the actual condition of the finished gloves. The component contains P image acquisition devices to acquire images of the finished rubber gloves from different angles, where P represents the number of image acquisition devices. The image acquisition device component has at least one image acquisition device and may include multiple image acquisition devices to ensure comprehensive image acquisition of the gloves and provide data support for glove quality analysis.

[0035] Furthermore, embodiments of this application also include: Step S220: Extract the first image acquisition device from the P image acquisition devices; Step S230: Match the preset finished video with the first finished video of the first image acquisition device; Step S240: The first finished video is extracted based on a preset video extraction scheme to obtain a first key finished video, wherein the first key finished video includes Q key image frames, where Q is an integer greater than 1; Step S250: Preprocess the Q key image frames to obtain the target key image frames; Step S260: Perform image processing analysis on the target key image frame to obtain the integrity result of the first finished product; Step S270: Obtain the preset finished product integrity result based on the first finished product integrity result.

[0036] Specifically, during the image acquisition process of the finished rubber gloves, multiple image acquisition devices are arranged according to quality standards and actual acquisition requirements, performing video acquisition and inspection of the gloves from different angles. Each image acquisition device is equipped with multiple sub-devices, each with a unique number, categorized as a first image acquisition device, a second image acquisition device, a third image acquisition device, etc., responsible for video storage, main camera, secondary camera, backup, etc., respectively. The first image acquisition device stores the video captured by the image acquisition device at a specific angle of the glove, forming the first finished product video. The quality analysis system uses the OpenCV library in Python to call the first image acquisition device number of each image acquisition device, obtaining the first finished product video from the first image acquisition device, and assembling it into a preset finished product video.

[0037] The preset video capture scheme refers to a pre-set plan based on the positional spacing between gloves on the production line to ensure accurate image capture of each glove. First, a camera is installed, and the center point of a specific glove angle within the camera's view is determined. Then, a preset displacement threshold is set, and the distance between two adjacent rubber gloves is measured as the preset displacement threshold. Finally, the glove movement speed on the production line is determined, and the time interval between adjacent inspected gloves appearing in the video is determined based on the distance and speed. Key image frames are extracted from the first finished product video based on this time interval. Key image frames from different angles constitute the first key finished product video. Here, a key image frame refers to the image frame captured at the time interval point, and Q refers to the number of key image frames in the first key finished product video. Q is an integer greater than 1, meaning the first key finished product video contains at least one key image frame for quality analysis of the rubber gloves.

[0038] After obtaining the key image frames, they are preprocessed using image registration technology. Images of the finished glove from different angles are combined and matched to obtain the overall image of the glove. This overall image after registration is called the target key image frame. Next, image processing and analysis are performed on the target key image frame. For example, edge detection algorithms are used to identify and detect damaged areas in the image; color space conversion is performed, such as RGB to LAB conversion, to calculate the color difference of corresponding pixels; and line detection methods are used to obtain the size information of objects in the image and calculate the deviation from preset values. Based on the system algorithm, a first finished product integrity result is generated for the target image frame. This result includes color evaluation, defect evaluation, size evaluation, and light transmittance evaluation. Based on the evaluation results, a preset finished product integrity result is obtained, which includes qualified and unqualified results. Qualified results proceed to the next step of the production process, while unqualified results are processed or repaired.

[0039] The finished gloves are captured by image acquisition equipment, and the video is decomposed and integrated by image processing technology. The detection is carried out by edge detection, line detection and other methods, which greatly improves the detection efficiency and ensures the accuracy of the analysis results.

[0040] Furthermore, such as Figure 2 As shown, embodiments of this application also include: Step S251: Extract the first frame image from the Q key image frames and use the first frame image as the registration reference image; Step S252: Obtain a set of non-first frame images after removing the registration reference images from the Q key image frames, wherein the set of non-first frame images includes R non-first frame images, where R is an integer greater than or equal to 1; Step S253: Use the R non-first frame images as the images to be registered; Step S254: Based on the registration reference image, the image to be registered is offset and registered using the feature point matching principle to obtain the target key image frame.

[0041] Specifically, when acquiring images of the finished rubber gloves, absolute registration is used for image registration. The palm side of the glove is considered the front, and the image captured at that angle is the first frame. OpenCV in Python is used to extract the corresponding first frame image from Q key image frames, which serves as the registration reference image. Key image frames from other angles after extracting the first frame image form a non-first frame image set. R refers to the number of non-first frame images in the non-first frame image set, and R is an integer greater than 1, meaning the non-first frame image set contains at least one non-first frame image. These R non-first frame images are used as the images to be registered.

[0042] Feature point matching involves identifying identical feature points in two or more images and comparing the similarity between the descriptors of each feature point to determine if they match. Higher similarity increases the likelihood of a match. Local feature images are then matched into a unified image for subsequent processing. Specifically, after determining the registration reference image and the image to be registered, the SIFT feature matching algorithm is first used to find similar feature points in the image to be registered and the registration reference image, such as corners, edges, and textures, for image registration. Then, by calculating the correspondence of similar feature points, the offset of the image to be registered relative to the registration reference image is obtained. This offset is used to perform translation, rotation, and other operations on the image to be registered to align it with the registration reference image. Finally, after registration, the image to be registered can be converted into an image in the same coordinate system as the registration reference image, which is the target keyframe. Image matching technology can eliminate offsets caused by different shooting angles, shooting nodes, etc., improving the accuracy of subsequent processing.

[0043] Furthermore, embodiments of this application also include: Step S510: Analyze the preset subjective standards and construct a set of subjective factor indicators; and Step S520: Obtain historical glove quality analysis records and analyze them to build a correlation test database; Step S530: Based on the subjective factor index set, traverse the correlation test database to obtain the subjective factor index parameter set; Step S540: Obtain the finished product quality index from the correlation test database, wherein the finished product quality index has a corresponding relationship with the set of subjective factor index parameters; Step S550: Use the set of subjective factor index parameters as independent variables and the finished product quality index as dependent variables; Step S560: Perform correlation analysis on the independent variable and the dependent variable, and reduce the set of subjective factor indicators according to the analysis results to obtain the preset factor indicators.

[0044] Specifically, the evaluation indicators are determined based on preset subjective standards. These are variables that can objectively reflect the specific manifestations of the subjective standards, such as subjective feelings like feel and fit. Subjective feelings are then converted into objective indicators based on existing finished products, such as component content and stretchability. The indicators are then weighted, with weights determined through methods such as expert judgment and questionnaires. After determining the evaluation indicators and their corresponding weights, a weighted score is calculated, constructing a set of subjective factor indicators.

[0045] Historical glove quality analysis records include internal production data and data provided by suppliers, including the data recording time, glove production batch, production line, and production process. The collected data is reviewed and screened to remove invalid and duplicate data. Simultaneously, data cleaning and summarization are performed, and the data is organized into an easily analyzable format. Based on a set of subjective factor indicators, a correlation test database is constructed. The database fields include indicator names, indicator parameters, weight coefficients, and correlation coefficients, etc. The correlation analysis results are then stored in the database, resulting in the correlation test database.

[0046] Using a programming language, an SQL query script was written to iterate through each record in the correlation test database based on the name of the subjective factor indicator. The script extracted the indicator parameters, weight coefficients, correlation coefficients, and corresponding finished product quality indices to obtain the parameters of each subjective factor indicator in the historical glove quality analysis records. The finished product quality index is an indicator reflecting the overall quality of a glove, derived from a comprehensive evaluation of multiple aspects of historical finished gloves. It is a combination of objective and subjective factor indicator parameter sets. Each historical glove quality analysis result corresponds to a finished product quality index, and the finished product quality index has a corresponding set of subjective factor indicator parameters.

[0047] In the quality analysis of gloves, a set of subjective factor indicators is used to evaluate the finished product quality index. The finished product quality index depends on this set of subjective factor indicators; therefore, the set of subjective factor indicators is used as the independent variable, and the finished product quality index as the dependent variable. Statistical software is used to perform correlation analysis on the set of subjective factor indicators and the finished product quality index to identify key subjective factor indicators affecting finished product quality. Based on the correlation, these subjective factor indicators are categorized and reduced to determine those with a greater impact on finished product quality. Based on this, a reverse matching of the subjective factor indicators is used to derive the pre-set factor indicators.

[0048] By establishing subjective factor indicators and retrieving historical glove quality analysis records to obtain a set of subjective factor indicator parameters, and conducting correlation analysis with the finished product quality index to obtain preset factor indicators, the subjective factors of human experience are objectified and standardized, reducing the misjudgment rate caused by subjective factors, while improving detection efficiency and increasing the accuracy of quality analysis.

[0049] Furthermore, such as Figure 3 As shown, embodiments of this application also include: Step S561: Obtain the correlation analysis results, wherein the correlation analysis results include multiple correlation information; Step S562: Extract the highly significant correlation information from the multiple correlation information and back-match the subjective factor index to obtain the first preset factor index; Step S563: Extract the significant correlation information from the multiple correlation information and back match the subjective factor index to obtain the second preset factor index; Step S564: The first preset factor index and the second preset factor index together constitute the preset factor index.

[0050] Specifically, Stata statistical software was used to perform correlation analysis on the set of subjective factor indicators and the finished product quality index to obtain correlation analysis results, including Pearson correlation coefficient and Spearman correlation coefficient. The Pearson correlation coefficient measures linear relationships, while the Spearman correlation coefficient measures non-linear relationships. In the coefficient results, 0 indicates no relationship between the two variables, while 1 and -1 indicate a perfect positive or negative correlation, respectively. The closer the correlation coefficient is to 1 or -1, the stronger the correlation between the two variables.

[0051] Correlation coefficients with an absolute value greater than 0.8 are considered highly significant, while those with an absolute value greater than 0.5 and less than 0.8 are considered significantly significant. Factor analysis is used to convert highly significant and significantly significant correlations into principal components. These principal components represent variables of different dimensions in the original data, and each principal component is a linear combination of subjective factors. Regression analysis is used to establish the relationship between the principal components and the finished product quality index. The principal components of highly significant correlations are back-matched to the original set of subjective factor indicators to determine the table of subjective factor indicators that have the greatest impact on the finished product quality index, which are the first pre-defined factor indicators. The principal components of significantly significant correlations are back-matched to the original set of subjective factor indicators to determine the table of subjective factor indicators that have the greatest impact on the finished product quality index, which are the second pre-defined factor indicators. Based on the first and second pre-defined factor indicators, the pre-defined factor indicators are determined. These pre-defined factor indicators provide data support for obtaining the subsequent pre-defined quality index, thereby improving the accuracy of the finished product quality analysis.

[0052] Furthermore, embodiments of this application also include: Step S710: The finished product quality index includes multiple quality indices; Step S720: Count the number of the multiple quality indices to obtain the total number of indices; Step S730: Obtain a first preset index threshold and filter the plurality of quality indices to obtain a first quality index set, wherein the first quality index set refers to the set of indices among the plurality of quality indices that meet the first preset index threshold. Step S740: Count the number of the first quality index concentration index to obtain the number of the first index; Step S750: Calculate the ratio of the first index quantity to the total index quantity to obtain the first ratio; Step S760: Obtain a preset ratio threshold and compare it with the first ratio to obtain a first comparison result; Step S770: Adjust the first preset index threshold according to the first comparison result to obtain the target index threshold; Step S780: Construct a quality index-level list based on the target index threshold; Step S790: The preset quality index is obtained by matching the preset quality index with the quality index-level list to obtain the preset quality level.

[0053] Specifically, the finished product quality index consists of multiple quality indices, including size index, surface quality index, color index, insulation index, and thickness index. Before production, each quality index of the gloves is defined according to the product's intended use and customer requirements. This definition serves as a first preset index threshold. For example, if the optimal size index, surface quality index, color index, insulation index, and thickness index of the finished gloves produced by the production line are 85, 90, 75, 40, and 75 respectively, the number of quality indices is counted, resulting in 365 indices. The size index, surface quality index, color index, insulation index, and thickness index within the first preset index threshold for this glove are 80~100, 85~100, 80~100, 30~70, and 60~80 respectively, with a preset ratio threshold of 0.8.

[0054] The quality indices of the finished products are screened based on a first preset index threshold. Indices whose size, color, insulation, and thickness indices are greater than the first preset index threshold are considered to meet it. Therefore, the first quality index set (size, color, insulation, and thickness indices) has a total of 290 indices, which is the first index quantity. The ratio of the first index quantity (290) to the index quantity (365) is approximately 0.79, which is the first ratio.

[0055] According to the product quality standards, the preset ratio threshold is 0.8. This is compared with the first ratio of 0.79 for the finished gloves, yielding a first comparison result: the preset ratio threshold is not met. Based on this first comparison result, the first preset index threshold is adjusted. In this example, the color index is reduced to 75. Therefore, the target index thresholds are: size index 80, surface quality index 85, color index 75, insulation index 30, and thickness index 60.

[0056] The index is divided into 5 levels based on the target index threshold: the fifth level has a quality index of 66, the fourth level has a quality index of 132, the third level has a quality index of 198, the second level has a quality index of 264, and the first level has a quality index of 330. The smaller the level number, the better the quality. A quality index-level list is constructed to determine the preset quality level of the product.

[0057] Within specific product lines, weights can be set according to the importance of each index, reflecting the quality priority of the indices and further improving accuracy. By classifying the quality of rubber gloves into grades, the analysis results are simplified, analytical efficiency is improved, and basic data is provided for subsequent quality analysis of rubber gloves.

[0058] Furthermore, embodiments of this application also include: Step S771: If the first ratio is greater than the preset ratio threshold, generate a reduction instruction; Step S772: Adjust the first preset index threshold based on the reduction instruction to obtain a first adjustment result, and perform iterative analysis on the first adjustment result until the target index threshold is obtained; Step S773: If the first ratio is less than the preset ratio threshold, generate an expansion instruction; Step S774: Based on the expansion instruction, the first preset index threshold is expanded and adjusted to obtain a second adjustment result, and the second adjustment result is iteratively analyzed until the target index threshold is obtained; Step S775: If the first ratio is within the preset ratio threshold, the first preset index threshold is used as the target index threshold.

[0059] Specifically, firstly, a first preset index threshold is established based on the intended use of the gloves. This first preset index threshold refers to an initial index threshold established based on production experience and product requirements, and is not used as a production standard threshold. Then, rubber glove samples are produced according to product requirements, high-quality samples are selected, and various quality indices of the rubber glove samples are tested. The total index is calculated, and the quality indices that meet the first preset index threshold are selected to calculate the first index quantity, thereby obtaining the first ratio.

[0060] First, product developers develop and code automated scripts for threshold adjustment. Then, the automated test script is run to adjust the first preset index threshold based on the first ratio to generate the target index threshold.

[0061] When the first ratio is greater than the preset ratio threshold, it indicates that the index threshold is too large. The algorithm generates a reduction instruction to adjust the threshold. Specifically, it first calculates the degree to which the threshold needs to be reduced based on the current threshold and ratio, converts the reduction degree into a reduction instruction, and adjusts the first preset index threshold. Then, it re-performs the technique to obtain the first adjustment result, and then calculates whether further adjustment is needed until the target index threshold is obtained.

[0062] When the first ratio is less than the preset ratio threshold, it means that the index threshold is too small. The algorithm generates an expansion instruction to adjust the threshold. Specifically, it first calculates the degree to which the threshold needs to be expanded based on the current threshold and ratio, converts the expansion degree into an expansion instruction, expands and adjusts the first preset index threshold, and then re-performs the technique to obtain the first adjustment result. Then it calculates whether further adjustment is needed until the target index threshold is obtained.

[0063] When the first ratio is within the preset ratio threshold, the first preset index threshold is used as the target index threshold.

[0064] By determining the target index threshold for rubber glove sample products, the optimal threshold and index level can be identified, greatly improving the testing efficiency of rubber gloves and enhancing the accuracy of the quality index, thereby improving the overall accuracy of quality testing.

[0065] In summary, the method for analyzing the molding quality of rubber gloves has the following technical advantages: The process involves obtaining the intended use of pre-defined rubber gloves and matching them with pre-defined glove standards to establish production standards for the gloves. This facilitates subsequent analysis of the finished gloves based on these standards. Video capture of the finished rubber gloves is performed using an image acquisition device, resulting in a pre-defined finished product video. Analysis of this video yields a pre-defined finished product integrity result. Compared to manual inspection, the use of computer vision and image processing technologies significantly improves inspection efficiency and allows for rapid determination of the finished product integrity result through image detection. When the pre-defined finished product integrity result indicates that the finished product is intact, a pre-defined objective standard is invoked to perform a quality analysis of the pre-defined rubber gloves, yielding a pre-defined objective indicator analysis result. When the pre-defined objective indicator analysis result indicates that the objective indicators are qualified, a pre-defined subjective standard is invoked to perform a quality analysis of the pre-defined rubber gloves, yielding a pre-defined subjective indicator analysis result. This process of sequentially evaluating the finished gloves for integrity, objective indicators, and subjective indicators continuously filters out gloves that meet the requirements, improving the accuracy of the glove quality analysis. Preset factor indicators are obtained and traversed through the analysis results of preset subjective indicators to obtain preset factor indicator parameters. These parameters correspond to the preset factor indicators. Setting preset factor indicators for the product objectifies and standardizes subjective human factors, improving analysis efficiency and further enhancing the accuracy of quality analysis. Preset weight coefficients for the preset factor indicators are obtained by analyzing the preset factor indicator parameters, and a preset quality index is calculated based on these weighted parameters. A preset quality level is then matched based on the preset quality index, and this preset quality level is used for quality analysis of the preset rubber gloves. Quality levels are then classified according to the quality index, further subdividing the product and improving the accuracy of quality analysis.

[0066] Example 2

[0067] Based on the same inventive concept as the molding quality analysis method for a rubber glove in the foregoing embodiments, such as Figure 4 As shown, this application also provides a molding quality analysis system for rubber gloves, wherein the system includes: A preset data acquisition module is used to acquire the preset intended use of a preset rubber glove and match it with preset glove standards, wherein the preset glove standards include preset subjective standards and preset objective standards; and The image acquisition and analysis module uses an image acquisition device component to acquire video of the finished product of the preset rubber glove, obtains a video of the preset finished product, and analyzes the video of the preset finished product to obtain a result of the integrity of the preset finished product. An objective standard analysis module is used to call the preset objective standard to perform quality analysis on the preset rubber gloves when the preset finished product integrity result is that the finished product is complete, and to obtain the preset objective index analysis result. The subjective standard analysis module is used to call the preset subjective standard to perform quality analysis on the preset rubber glove when the preset objective indicator analysis result is qualified, and obtain the preset subjective indicator analysis result. The preset factor index parameter module is used to obtain preset factor indicators and iterate through the preset subjective index analysis results to obtain preset factor index parameters, wherein the preset factor index parameters have a corresponding relationship with the preset factor indicators. The preset quality index module is used to analyze the preset factor index parameters to obtain the preset weight coefficients of the preset factor index, and to calculate the preset quality index by weighting the preset factor index parameters. A preset quality level module is provided, which matches a preset quality level based on the preset quality index, wherein the preset quality level is used to perform quality analysis on the preset rubber gloves.

[0068] Furthermore, embodiments of this application also include: The image acquisition device component module refers to a module equipped with P image acquisition devices, where P is an integer greater than or equal to 1.

[0069] Furthermore, embodiments of this application also include: An image extraction module is used to extract the first image acquisition device among the P image acquisition devices; The video matching module is used to match the preset finished video with the first finished video of the first image acquisition device; A key finished product video module, which extracts the first finished product video based on a preset video extraction scheme to obtain a first key finished product video, wherein the first key finished product video includes Q key image frames, where Q is an integer greater than 1; The target key image frame module is used to preprocess the Q key image frames to obtain the target key image frame. The first finished product integrity result module is used to perform image processing and analysis on the target key image frame to obtain the first finished product integrity result. A preset finished product integrity result module, wherein the preset finished product integrity result is used to obtain the preset finished product integrity result based on the first finished product integrity result.

[0070] Furthermore, embodiments of this application also include: A registration reference image module is used to extract the first frame image from the Q key image frames and use the first frame image as the registration reference image. The non-first-frame image set module is used to obtain a non-first-frame image set after removing the registration reference image from the Q key image frames, wherein the non-first-frame image set includes R non-first-frame images, where R is an integer greater than or equal to 1; The image to be registered module is used to take the R non-first frame images as images to be registered; The target key image frame module is used to perform offset registration on the image to be registered based on the registration reference image and using the feature point matching principle to obtain the target key image frame.

[0071] Furthermore, embodiments of this application also include: The subjective factor index module, wherein the subjective factor index is used to analyze the preset subjective standard and construct a set of subjective factor indicators; and A correlation test database module is used to acquire historical glove quality analysis records and analyze them to construct a correlation test database. The subjective factor indicator parameter module is used to traverse the correlation test database based on the subjective factor indicator set to obtain the subjective factor indicator parameter set. The finished product quality index module is used to obtain the finished product quality index from the correlation test database, wherein the finished product quality index has a corresponding relationship with the set of subjective factor index parameters. A variable setting module is used to take the set of subjective factor index parameters as independent variables and the finished product quality index as dependent variables. The preset factor index module is used to perform correlation analysis between the independent variable and the dependent variable, and to reduce the set of subjective factor indicators based on the analysis results to obtain the preset factor index.

[0072] Furthermore, embodiments of this application also include: A correlation analysis module is used to obtain correlation analysis results, wherein the correlation analysis results include multiple correlation information. The first preset factor index module is used to extract highly significant correlation information from the multiple correlation information and back match subjective factor indexes to obtain the first preset factor index. The second preset factor index module is used to extract significant correlation information from the multiple correlation information and back match subjective factor indexes to obtain the second preset factor index. A preset factor index module is provided, wherein the preset factor index is composed of the first preset factor index and the second preset factor index.

[0073] Furthermore, embodiments of this application also include: The quality index module divides the finished product quality index into multiple quality indices. The index total module is used to count the number of the multiple quality indices and obtain the total index. The first quality index set module is used to obtain a first preset index threshold and filter the plurality of quality indices to obtain a first quality index set, wherein the first quality index set refers to the set of indices among the plurality of quality indices that meet the first preset index threshold. The first index quantity module is used to count the number of the first quality index concentration index to obtain the first index quantity. The first ratio module is used to calculate the ratio of the first index quantity to the total index quantity to obtain the first ratio. The first comparison result module is used to obtain a preset ratio threshold and compare it with the first ratio to obtain a first comparison result; A target index threshold module is used to adjust the first preset index threshold according to the first comparison result to obtain a target index threshold. A quality level module, which is used to construct a quality index-level list based on the target index threshold; A quality level matching module is used to obtain the preset quality level by matching the preset quality index based on the quality index-level list.

[0074] Furthermore, embodiments of this application also include: A reduction instruction module, wherein the reduction instruction module is used to generate a reduction instruction if the first ratio is greater than the preset ratio threshold; The first adjustment result module adjusts the first preset index threshold based on the reduction instruction to obtain the first adjustment result, and iteratively analyzes the first adjustment result until the target index threshold is obtained. An expansion instruction module is configured to generate an expansion instruction if the first ratio is less than the preset ratio threshold. The second adjustment result module expands and adjusts the first preset index threshold based on the expansion instruction to obtain a second adjustment result, and iteratively analyzes the second adjustment result until the target index threshold is obtained. The ratio determination module is used to determine the target index threshold if the first ratio is within the preset ratio threshold.

[0075] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Figure 1 The molding quality analysis method and specific examples of a rubber glove in Embodiment 1 are also applicable to the molding quality analysis system of a rubber glove in this embodiment. Through the foregoing detailed description of the molding quality analysis method of a rubber glove, those skilled in the art can clearly understand the molding quality analysis system of a rubber glove in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here. For relevant parts, please refer to the method section.

[0076] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily 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 this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for analyzing the molding quality of rubber gloves, characterized in that, include: Obtain the intended use of the pre-defined rubber gloves and match them with pre-defined glove standards, wherein the pre-defined glove standards include pre-defined subjective standards and pre-defined objective standards; and The finished product of the preset rubber gloves is captured by an image acquisition device component to obtain a video of the preset finished product, and the integrity result of the preset finished product is obtained by analyzing the video of the preset finished product. When the preset finished product integrity result is that the finished product is complete, the preset objective standard is invoked to perform a quality analysis on the preset rubber gloves, and the preset objective index analysis result is obtained. When the preset objective indicator analysis result is qualified, the preset subjective standard is called to perform quality analysis on the preset rubber glove to obtain the preset subjective indicator analysis result. Preset factor indicators are obtained, and statistical software is used to perform correlation analysis on the set of subjective factor indicator parameters and the finished product quality index to obtain key subjective factor indicators that affect the quality of finished products. Subjective factor indicators are divided and reduced according to their correlation to obtain subjective factor indicators that have a greater impact on the quality of finished products. Based on this, the subjective factor indicators are back-matched to obtain the preset factor indicators. The preset factor indicators are then traversed through the preset subjective indicator analysis results to obtain the preset factor indicator parameters, wherein the preset factor indicator parameters and the preset factor indicators have a corresponding relationship. The preset weight coefficients of the preset factor indicators are obtained by analyzing the preset factor indicator parameters, and the preset quality index is obtained by weighting the preset factor indicator parameters together. A preset quality level is matched based on the preset quality index, wherein the preset quality level is used to perform quality analysis on the preset rubber gloves.

2. The molding quality analysis method according to claim 1, characterized in that, The image acquisition device assembly includes P image acquisition devices, where P is an integer greater than or equal to 1.

3. The molding quality analysis method according to claim 2, characterized in that, The analysis of the preset finished product video to obtain the preset finished product integrity result includes: Extract the first image acquisition device from the P image acquisition devices; Combine the preset finished video with the first finished video of the first image acquisition device; The first finished video is extracted based on a preset video extraction scheme to obtain a first key finished video, wherein the first key finished video includes Q key image frames, where Q is an integer greater than 1. The Q key image frames are preprocessed to obtain the target key image frames; Image processing and analysis are performed on the target key image frames to obtain the integrity result of the first finished product; The preset finished product integrity result is obtained based on the first finished product integrity result.

4. The molding quality analysis method according to claim 3, characterized in that, The preprocessing of the Q key image frames to obtain the target key image frames includes: Extract the first frame image from the Q key image frames and use the first frame image as the registration reference image; Obtain a set of non-first frame images after removing the registration reference images from the Q key image frames, wherein the set of non-first frame images includes R non-first frame images, where R is an integer greater than or equal to 1; The R non-first frame images are used as images to be registered; Based on the registration reference image, the image to be registered is offset and registered using the feature point matching principle to obtain the target key image frame.

5. The molding quality analysis method according to claim 1, characterized in that, The step of obtaining preset factor indicators and iterating through the preset subjective indicator analysis results to obtain preset factor indicator parameters includes: Analyze the preset subjective standards and construct a set of subjective factor indicators; and Obtain historical glove quality analysis records and analyze them to build a correlation test database; Based on the subjective factor index set, the subjective factor index parameter set is obtained by traversing the correlation test database. Obtain the finished product quality index from the correlation test database, wherein the finished product quality index has a corresponding relationship with the set of subjective factor index parameters; The set of subjective factor indicators is used as the independent variable, and the finished product quality index is used as the dependent variable. A correlation analysis is performed on the independent variable and the dependent variable, and the set of subjective factor indicators is reduced based on the analysis results to obtain the preset factor indicators.

6. The molding quality analysis method according to claim 5, characterized in that, The correlation analysis between the independent variable and the dependent variable, and the reduction of the set of subjective factor indicators based on the analysis results, to obtain the preset factor indicators, include: Obtain the correlation analysis results, wherein the correlation analysis results include multiple correlation information; Extract the highly significant correlation information from the multiple correlation information and back-match it with the subjective factor index to obtain the first preset factor index; Extract significant correlation information from the multiple correlation information and back match subjective factor indicators to obtain a second preset factor indicator; The first preset factor index and the second preset factor index together constitute the preset factor index.

7. The molding quality analysis method according to claim 5, characterized in that, The matching of preset quality levels based on the preset quality index includes: The finished product quality index includes multiple quality indices; The total number of the multiple quality indices is obtained by counting the number of indices. Obtain a first preset index threshold and filter the plurality of quality indices to obtain a first quality index set, wherein the first quality index set refers to the set of indices among the plurality of quality indices that meet the first preset index threshold; The number of concentration indices of the first quality index is counted to obtain the number of the first index. Calculate the ratio of the first index quantity to the total index quantity to obtain the first ratio; Obtain a preset ratio threshold and compare it with the first ratio to obtain a first comparison result; The first preset index threshold is adjusted based on the first comparison result to obtain the target index threshold; Construct a quality index-level list based on the target index threshold; The preset quality index is obtained by matching the preset quality index-level list to obtain the preset quality level.

8. The molding quality analysis method according to claim 7, characterized in that, The step of adjusting the first preset index threshold based on the first comparison result to obtain the target index threshold includes: If the first ratio is greater than the preset ratio threshold, a reduction instruction is generated; The first preset index threshold is adjusted based on the reduction instruction to obtain a first adjustment result, and the first adjustment result is iteratively analyzed until the target index threshold is obtained. If the first ratio is less than the preset ratio threshold, an expansion instruction is generated; The first preset index threshold is expanded and adjusted based on the expansion instruction to obtain a second adjustment result, and the second adjustment result is iteratively analyzed until the target index threshold is obtained. If the first ratio is within the preset ratio threshold, the first preset index threshold is used as the target index threshold.

9. A molding quality analysis system for rubber gloves, characterized in that, include: A preset data acquisition module is used to acquire the preset intended use of a preset rubber glove and match it with preset glove standards, wherein the preset glove standards include preset subjective standards and preset objective standards; and The image acquisition and analysis module uses an image acquisition device component to acquire video of the finished product of the preset rubber glove, obtains a video of the preset finished product, and analyzes the video of the preset finished product to obtain a result of the integrity of the preset finished product. An objective standard analysis module is used to call the preset objective standard to perform quality analysis on the preset rubber gloves when the preset finished product integrity result is that the finished product is complete, and to obtain the preset objective index analysis result. The subjective standard analysis module is used to call the preset subjective standard to perform quality analysis on the preset rubber glove when the preset objective indicator analysis result is qualified, and obtain the preset subjective indicator analysis result. The module for preset factor index parameters is used to acquire preset factor indicators. It then uses statistical software to perform correlation analysis on the set of subjective factor index parameters and the finished product quality index to obtain key subjective factor indicators affecting finished product quality. Based on the correlation, the subjective factor indicators are divided and reduced to obtain subjective factor indicators with a greater impact on finished product quality. Based on this, the preset factor indicators are obtained by reverse matching. Finally, the preset factor indicators are iterated through the preset subjective indicator analysis results to obtain preset factor indicator parameters, wherein the preset factor indicator parameters and the preset factor indicators have a corresponding relationship. The preset quality index module is used to analyze the preset factor index parameters to obtain the preset weight coefficients of the preset factor index, and to calculate the preset quality index by weighting the preset factor index parameters. A preset quality level module is provided, which matches a preset quality level based on the preset quality index, wherein the preset quality level is used to perform quality analysis on the preset rubber gloves.

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