Glass product quality abnormity tracking management interaction method and system and medium

Abstract

The invention provides a glass product quality abnormity tracking management interaction method and system and a medium, and relates to the technical field of glass production management. According to the method, firstly, a main interface integrating data acquisition, quality abnormity tracking and quality risk early warning functions is displayed, and a user can execute specific operation by triggering a corresponding control. The data acquisition sub-interface supports the input of original melt detection, thermal detection and machine testing data to form basic data for quality detection; the quality anomaly tracking sub-interface realizes comprehensive management of quality anomaly events based on the basic data, including defect isolation, recall, tracing and tracking, and generates detailed anomaly processing records and quality anomaly analysis reports; the quality risk early-warning sub-interface can push the production line quality risk early-warning information according to the preset risk threshold value, and displays related exception handling records and analysis reports in a linkage manner, so that the quality management accuracy is effectively improved, and the overall production efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of glass production management technology, and in particular to an interactive method, system and medium for tracking and managing abnormal quality of glass products. Background Technology

[0002] Quality control is a crucial aspect of glass product manufacturing. To ensure product quality, companies typically conduct a series of quality inspections and management activities, including but not limited to molten metal testing, thermal testing, and trial run data entry. While existing quality management methods and technologies can support these processes to some extent, they still have shortcomings. Traditional quality management systems lack integration, potentially leading to information silos and impacting overall efficiency and data consistency. For existing quality issues, systems may struggle to quickly and accurately pinpoint their root causes. This means that problem-solving can be lengthy, and completely preventing recurrence is difficult. Summary of the Invention

[0003] The purpose of this invention is to provide an interactive method, system, and medium for tracking and managing abnormal quality of glass products, in order to solve one or more technical problems existing in the prior art, or at least provide a beneficial option or create conditions.

[0004] The solution to the technical problem of this invention is as follows: On the one hand, this invention provides an interactive method for tracking and managing quality anomalies in glass products, comprising the following steps: The main interface for tracking and managing quality anomalies in glass products is displayed; the main interface includes data acquisition controls, quality anomaly tracking controls, and quality risk warning controls. In response to the trigger command of the data acquisition control, the data acquisition sub-interface is displayed to enter the original melt test data, thermal test data and test data to generate basic quality test data; In response to the trigger command of the quality anomaly tracking control, the quality anomaly tracking sub-interface is displayed, a quality anomaly event is initiated based on the quality inspection basic data, defect isolation, recall, tracing and tracking operations are performed to generate anomaly handling records, and defect type statistics, defect cause root cause analysis and inspection equipment data analysis are performed to generate a quality anomaly analysis report. In response to the trigger command of the quality risk warning control, the quality risk warning sub-interface is displayed. Based on the basic quality inspection data and the preset risk threshold, the production line quality risk warning information is pushed, and the corresponding abnormal handling records and quality abnormality analysis reports are displayed in conjunction.

[0005] Furthermore, the data acquisition sub-interface includes a work order selection control and a test data entry control; In the data acquisition sub-interface, the original melt test data, thermal test data, and trial run data are entered to generate basic quality inspection data, including the following steps: In response to the trigger command of the work order selection control, a work order selection window is displayed, and the target work order for which test data is to be entered is selected; In response to the trigger command of the detection data entry control, the detection data entry window is displayed, and the original melt composition data, thermal test parameter data and test result data of the target work order are entered to generate the basic quality inspection data of the target work order.

[0006] Furthermore, the data acquisition sub-interface also includes a detection item configuration control; in the data acquisition sub-interface, custom detection items and defect comparison tables are configured, defect types are added, judgment criteria are modified, and redundant detection items are deleted, and the configuration results are synchronized to the detection equipment, including the following steps: In response to the trigger command of the inspection item configuration control, the inspection item configuration window is displayed, allowing for the addition, modification, or deletion of custom inspection items, and maintaining a table of defect types and judgment criteria; In response to the configuration completion command, the updated inspection item configuration and defect comparison table are synchronized to the associated inspection equipment to ensure that the data entry fields are consistent with the equipment inspection parameters.

[0007] Furthermore, the quality anomaly tracking sub-interface includes an anomaly event initiation control and a quality anomaly analysis control; In the quality anomaly tracking sub-interface, a quality anomaly event is initiated based on the quality inspection basic data. Defect isolation, recall, tracing, and tracking operations are performed to generate anomaly handling records. Furthermore, defect type statistics, defect cause analysis, and inspection equipment data analysis are conducted to generate a quality anomaly analysis report, including the following steps: In response to the trigger command of the abnormal event initiation control, an abnormal event initiation window is displayed, and the quality abnormal event is initiated based on the quality inspection basic data to perform defect isolation, recall, tracing and tracking operations to generate the abnormal handling record; The exception handling record includes isolation tray information, recall execution log, traceability detection results, and tracking progress information; In response to the trigger command of the quality anomaly analysis control, the quality anomaly analysis window is displayed. Based on the anomaly handling record and the quality inspection basic data, defect type statistics, defect cause root cause analysis and inspection equipment data analysis are performed to generate the quality anomaly analysis report. The quality anomaly analysis report includes a defect distribution trend chart, a defect root cause map, and equipment rejection rate data.

[0008] Furthermore, the abnormal event initiation window includes a defect isolation control, a defect recall control, a defect tracing control, a defect tracking control, and an abnormal handling record saving control; In the abnormal event initiation window, the quality abnormal event is initiated based on the quality inspection basic data, and defect isolation, recall, tracing and tracking operations are performed to generate the abnormal handling record, including the following steps: In response to the trigger command of the defect isolation control, a defect isolation window is displayed, isolation pallet information is set, target work orders are automatically associated, and an isolation notification is generated and pushed to the warehouse management system; wherein, the isolation notification includes a defect description, isolation quantity, defect severity, and target warehouse location information; In response to the trigger command of the defect recall control, a defect recall window is displayed and a recall execution log is generated; the recall execution log is used to record the batch, quantity and processing progress of the recalled products. In response to the trigger command of the defect tracing control, a defect tracing window is displayed, and the tracing detection results are recorded; the tracing detection results are used to locate the abnormality generation process and its scope of influence. In response to a trigger command on the defect tracking control, a defect tracking window is displayed, and the tracking progress information is updated; the tracking progress information is used to monitor the exception handling process nodes and completion status. In response to a trigger command to the exception handling record saving control, the isolation tray information, the recall execution log, the trace detection result, and the tracking progress information are saved, and the exception handling record is generated.

[0009] Furthermore, the quality anomaly analysis window includes defect statistical analysis controls, equipment data analysis controls, and report generation controls; In the quality anomaly analysis window, based on the anomaly handling records and the basic quality inspection data, defect type statistics, defect cause root cause analysis, and inspection equipment data analysis are performed to generate the quality anomaly analysis report, including the following steps: In response to the trigger command of the defect statistical analysis control, the defect statistical analysis window is displayed, defect type frequency statistics and cause correlation analysis are performed, and defect distribution trend map and defect root cause map are generated. The defect distribution trend map is used to show the frequency change trend of various types of defects in different periods, and the defect root-finding map is used to locate the key process or material factors that cause defects. In response to the trigger command of the device data analysis control, the device data analysis window is displayed to perform correlation analysis and processing between device operating parameters and rejection data, and to obtain device rejection rate data; The equipment rejection rate data is used to evaluate the accuracy and anomaly identification capabilities of the detection equipment. In response to a trigger command on the report generation control, the anomaly handling record, the defect distribution trend map, the defect root cause map, and the equipment rejection rate data are associated to generate the quality anomaly analysis report.

[0010] Furthermore, the defect statistical analysis window includes a data filtering control, a defect trend chart generation control, and a defect root cause map generation control; In the defect statistics and analysis window, defect type frequency statistics and cause correlation analysis are performed to generate defect distribution trend charts and defect root cause maps, including the following steps: In response to the trigger command of the data filtering control, a data filtering window is displayed, and the statistical period, defect type dimension and sample range parameters are selected as data filtering conditions; In response to the trigger command of the defect trend map generation control, the defect trend map generation window is displayed, the time series granularity, data aggregation method and abnormal threshold marking rules are set, and the defect distribution trend map is generated in combination with the data filtering conditions; In response to the trigger command of the defect root-seeking map generation control, the root-seeking analysis window is displayed, the root-seeking analysis algorithm is executed, and the defect root-seeking map is generated and displayed.

[0011] Furthermore, the quality risk early warning sub-interface includes a risk early warning threshold setting control and an early warning information push control; On the quality risk early warning sub-interface, based on the basic quality inspection data and preset risk thresholds, production line quality risk early warning information is pushed out, and corresponding anomaly handling records and quality anomaly analysis reports are displayed in conjunction, including the following steps: In response to the trigger command of the risk warning threshold setting control, a threshold configuration window is displayed to set the defect rate threshold, equipment parameter deviation threshold, and raw material fluctuation threshold; In response to the trigger command of the warning information push control, a warning push window is displayed. Based on the basic quality inspection data and the preset risk threshold, the production line quality risk warning information is generated, and the corresponding abnormality handling records and quality abnormality analysis reports are displayed in conjunction with the warning information push control and sent to the designated terminal.

[0012] On the other hand, this application provides a glass product quality anomaly tracking and management interactive system for executing the aforementioned glass product quality anomaly tracking and management interactive method.

[0013] On the other hand, this application provides a computer-readable storage medium storing a processor-executable program, which, when executed by a processor, is used to implement the aforementioned glass product quality anomaly tracking and management interactive method.

[0014] The beneficial effects of this invention are as follows: This application provides an interactive method for tracking and managing quality anomalies in glass products. First, a main interface integrating data acquisition, quality anomaly tracking, and quality risk early warning functions is displayed. Users can execute specific operations by triggering corresponding controls. The data acquisition sub-interface supports the input of original melt detection, thermal inspection, and trial run data, forming the basic data for quality inspection. The quality anomaly tracking sub-interface, based on this basic data, achieves comprehensive management of quality anomaly events, including defect isolation, recall, tracing, and tracking, and generates detailed anomaly handling records and quality anomaly analysis reports. The quality risk early warning sub-interface can push production line quality risk early warning information according to preset risk thresholds, while simultaneously displaying relevant anomaly handling records and analysis reports, thereby effectively improving the accuracy of quality management and overall production efficiency. This application also provides a corresponding system and medium; the beneficial effects of the system and medium are similar to those of the method and will not be elaborated here.

[0015] Other features and advantages of this application will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the application. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the description, claims and drawings. Attached Figure Description

[0016] The accompanying drawings are provided to further understand the technical solutions of the present invention and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the technical solutions of the present invention, and do not constitute a limitation on the technical solutions of the present invention.

[0017] Figure 1 This is a flowchart of the glass product quality anomaly tracking and management interaction method provided in this application; Figure 2 This is a schematic diagram of the main interface for tracking and managing glass product quality anomalies provided in this application; Figure 3 This is a schematic diagram of the data acquisition sub-interface provided in this application; Figure 4 This is a schematic diagram of the exception event initiation window provided in this application; Figure 5 This is a schematic diagram of the quality anomaly analysis window provided in this application; Figure 6 This is a schematic diagram of the quality risk warning sub-interface provided in this application; Figure 7 This is a structural diagram of the glass product quality anomaly tracking and management interactive system provided in this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0019] The present application will be further described below with reference to the accompanying drawings and specific embodiments. The described embodiments should not be considered as limitations on the present application, and all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of the present application.

[0020] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0022] In the production of glass products, quality control is a crucial step. To ensure high-quality standards, companies typically conduct a series of quality inspections and management activities, including but not limited to raw melt testing, thermal testing data entry, and trial run record retrieval. Effective quality management not only improves product pass rates and reduces scrap rates but also enhances customer satisfaction and builds a positive market reputation for the company.

[0023] While existing quality management systems and technologies can support the above processes to some extent, they still have some limitations and shortcomings. Traditional quality management systems often consist of multiple independent modules, each responsible for a specific type of data collection or analysis (e.g., separate raw melt testing, thermal testing data entry). This approach makes it difficult to effectively integrate data between modules, creating so-called "information silos," which affects overall efficiency and data consistency.

[0024] When quality issues arise, there is a significant time delay between problem detection and action taken. This not only increases the risk of defective products leaving the market but also leads to more resources being wasted on tracking and fixing the problem. In the face of rapidly changing market demands, such delays can cause substantial losses for businesses.

[0025] For existing quality issues that have already occurred, current systems may struggle to quickly and accurately pinpoint their root causes. This means that problem-solving can be lengthy, and it's difficult to completely prevent similar issues from recurring. This poses a challenge to continuous product quality improvement. Many current systems lack effective risk warning capabilities, failing to identify potential quality hazards in advance. Therefore, in practice, they often resort to reactive measures rather than proactive prevention. An effective early warning system can help companies take action before problems escalate, thereby reducing losses. Existing systems have limited capabilities in in-depth analysis of quality data; for example, they struggle to accurately grasp the changing trends in the frequency of various defects across different periods, and they are not adept at identifying key processes or material factors that lead to defects. This limits companies' ability to optimize production processes and improve product quality through data analysis.

[0026] To address the shortcomings of existing technologies, this application proposes an interactive method, system, and medium for tracking and managing quality anomalies in glass products. This method integrates data acquisition, quality anomaly tracking, and quality risk early warning functions through a unified platform interface. It enables real-time input and analysis of original melt detection, thermal inspection, and trial run data, and can quickly initiate quality anomaly events for defect isolation, recall, tracing, and tracking operations, generating detailed anomaly handling records. The system also possesses powerful traceability capabilities and a comprehensive early warning system. Based on detailed data analysis, it can perform defect type statistics, root cause analysis, and equipment data analysis, automatically generating quality anomaly analysis reports and pushing production line quality risk early warning information. Furthermore, flexible data filtering and visualization tools support multi-dimensional in-depth analysis, helping to identify key process or material factors leading to defects, thereby optimizing production processes, improving overall quality management efficiency, ensuring timely and effective resolution of quality problems, and providing strong technical support for enterprises to achieve high-quality production goals.

[0027] First, the interactive method for tracking and managing quality anomalies in glass products provided in this application will be described in detail below with reference to the accompanying drawings.

[0028] Reference Figures 1 to 6 The implementation process of the glass product quality anomaly tracking and management interactive method provided in this application embodiment includes, but is not limited to, the following steps.

[0029] Step S110: Display the main interface 100 of the glass product quality anomaly tracking and management interaction.

[0030] Among them, reference Figure 2 The main interface 100 includes a data acquisition control 101, a quality anomaly tracking control 102, and a quality risk early warning control 103.

[0031] In step S110, the main interface 100 for tracking and managing glass product quality anomalies serves as the starting point for the entire quality management process. This main interface 100 integrates data acquisition controls 101, quality anomaly tracking controls 102, and quality risk early warning controls 103. This integrated interface design not only solves the problem of information silos in traditional systems but also provides users with a unified operating platform, enabling them to quickly access all relevant quality management tools and information on a single interface. This significantly improves work efficiency and ensures data consistency and integrity, laying a solid foundation for subsequent quality control work.

[0032] In step S120, in response to the trigger command of the data acquisition control 101, the data acquisition sub-interface 200 is displayed to enter the original melt detection data, thermal inspection data, and trial run data to generate basic quality inspection data.

[0033] In step S120, when the user triggers the data acquisition control 101, the system will display the data acquisition sub-interface 200, allowing the user to enter raw melt detection data, thermal inspection data, and trial run data, thereby generating basic quality inspection data. This process is crucial for ensuring the accuracy and timeliness of data at each key stage of production. By accurately recording this raw data, enterprises can better understand the production status, promptly identify potential problems, and provide reliable data support for subsequent quality analysis. Furthermore, real-time data entry also helps improve production efficiency and reduce decision-making delays caused by data lag.

[0034] In step S130, in response to the trigger command of the quality anomaly tracking control 102, the quality anomaly tracking sub-interface is displayed, a quality anomaly event is initiated based on the basic data of quality inspection, defect isolation, recall, tracing and tracking operations are performed to generate anomaly handling records, and defect type statistics, defect cause root cause analysis and inspection equipment data analysis are performed to generate a quality anomaly analysis report.

[0035] In step S130, once the quality anomaly tracking control 102 is triggered, the system displays a quality anomaly tracking sub-interface, initiating a quality anomaly event based on previously collected quality inspection data. In this interface, users can perform operations such as defect isolation, recall, tracing, and tracking to generate detailed anomaly handling records. The significance of this step lies in quickly addressing quality issues, preventing defective products from entering the market, and identifying the root cause of the problem through detailed recording and analysis. Simultaneously, by statistically analyzing defect types, conducting root cause analysis of defects, and analyzing testing equipment data, the system can automatically generate a quality anomaly analysis report, providing valuable improvement suggestions for enterprises, helping to optimize production processes and improve product quality.

[0036] In step S140, in response to the trigger command of the quality risk warning control 103, the quality risk warning sub-interface 500 is displayed. Based on the basic data of quality inspection and the preset risk threshold, the production line quality risk warning information is pushed, and the corresponding abnormal handling records and quality abnormality analysis reports are displayed in conjunction.

[0037] In step S140, when the user triggers the quality risk warning control 103, the system will display the quality risk warning sub-interface 500, pushing production line quality risk warning information based on basic quality inspection data and preset risk thresholds. This step aims to identify potential quality hazards in advance, enabling companies to take preventative measures before problems escalate. By linking and displaying corresponding anomaly handling records and quality anomaly analysis reports, users can obtain comprehensive information support to make more informed decisions. Furthermore, timely risk warnings help reduce losses, avoid unnecessary resource waste, further strengthen the company's risk management capabilities, and ensure the stability and continuity of production.

[0038] In some embodiments of this application, reference is made to Figure 3 The data acquisition sub-interface 200 includes a work order selection control 201 and a test data entry control 202. In step S120, the process of entering raw melt test data, thermal test data, and trial run data in the data acquisition sub-interface 200 to generate basic quality test data includes, but is not limited to, the following steps.

[0039] In step S210, in response to the trigger command of the work order selection control 201, the work order selection window is displayed, and the target work order for which the detection data is to be entered is selected.

[0040] In step S210, the target work order for which the test data is to be entered is selected, ensuring that the collection of quality data is closely related to the specific production task (i.e., the work order), thereby achieving orderly classification and traceability of the data. In the actual production process, each batch of glass products usually corresponds to an independent work order, containing key information such as production time, production line, operator, and raw material batch. By setting a work order selection control 201 in the data acquisition sub-interface 200, the user must select the corresponding target work order before entering quality data. The system then pops up a work order selection window, listing the currently available work orders for the user to filter. This design ensures that the subsequently entered test data corresponds one-to-one with the specific production batch, avoiding data confusion and unclear attribution, and providing an accurate data foundation for subsequent quality analysis, anomaly tracing, and risk warning. At the same time, it also lays a good data structure foundation for quality comparison and trend analysis between different batches.

[0041] In step S220, in response to the trigger command of the test data entry control 202, the test data entry window is displayed, the original melt composition data, thermal test parameter data and test result data of the target work order are entered, and the basic quality test data of the target work order is generated.

[0042] In step S220, a structured data entry window guides the user to input three main categories of quality inspection data related to the target work order: original melt composition data, thermal inspection parameter data, and trial run result data. Original melt composition data reflects the basic chemical composition of the glass raw materials and is a key factor affecting product quality stability. Thermal inspection parameter data records key process parameters during the high-temperature forming stage of production, such as temperature, pressure, and cooling rate, which directly affect the physical properties of the finished product. Trial run result data verifies the equipment's operating status and preliminary product quality during equipment debugging or new batch start-up, serving as an important basis for determining whether the production line is in a stable state. By uniformly inputting these three types of data into the system and binding them to the target work order, the system can generate complete and structured basic quality inspection data, serving as the original basis for subsequent quality anomaly tracking, defect analysis, and risk warning. This step not only improves the standardization and completeness of data entry but also enhances the usability and analytical value of quality data, providing solid data support for achieving full-process quality control.

[0043] In some embodiments of this application, reference is made to Figure 3 The data acquisition sub-interface 200 also includes a detection item configuration control 203. The process of configuring custom detection items and defect comparison tables, adding defect types, modifying judgment criteria, and deleting redundant detection items in the data acquisition sub-interface 200, and synchronizing the configuration results to the detection equipment, includes, but is not limited to, the following steps.

[0044] In step S310, in response to the trigger command of the inspection item configuration control 203, the inspection item configuration window is displayed, and the addition, modification or deletion of custom inspection items is performed, and a comparison table of defect types and judgment criteria is maintained.

[0045] In step S310, by adding, modifying, or deleting custom inspection items, the flexibility and configurability of the data acquisition sub-interface 200 are enhanced to adapt to dynamic changes in different product specifications, process requirements, and inspection standards. In actual production, different work orders may correspond to different types of glass products, such as flat glass, bottle glass, or special glass, and their inspection items and defect judgment standards often differ. Therefore, the system introduces an inspection item configuration control 203 into the data acquisition sub-interface 200, allowing quality management personnel or production line operators to flexibly add, modify, or delete inspection items according to current production needs.

[0046] For example, a new detection item for a specific dimensional deviation can be added, the judgment threshold for a certain type of bubble defect can be modified, or outdated redundant detection items can be deleted. Simultaneously, this step also supports the maintenance of a table comparing defect types with judgment criteria, ensuring that each type of defect (such as bubbles, inclusions, cracks, etc.) has a clear judgment basis. This configuration capability not only improves the system's adaptability and scalability but also ensures the accuracy and standardization of quality data entry, providing a standardized data foundation for subsequent quality analysis and anomaly handling.

[0047] In step S320, in response to the configuration completion command, the updated inspection item configuration and defect comparison table are synchronized to the associated inspection equipment to ensure that the data entry fields are consistent with the equipment inspection parameters.

[0048] In step S320, after completing the configuration of inspection items and the maintenance of the defect comparison table, the system needs to synchronize the updated configuration information to the associated automated inspection equipment, such as a spectrum analyzer, infrared thermal imager, and visual inspection system, via network or interface. This is to ensure that the quality data collected by the inspection equipment is completely consistent with the inspection item fields, defect classifications, and judgment criteria defined in the system, avoiding data errors, misjudgments, or missed detections caused by inconsistencies between equipment parameters and system configuration.

[0049] For example, when a redundant detection item is deleted from the system, the detection equipment should also simultaneously stop detecting that item to avoid generating invalid data; when a new defect type is added, the detection equipment should also update its identification model and judgment logic to match the latest configuration on the system side. Through the implementation of this step, the system achieves closed-loop management of software configuration and hardware execution, improves the automation level and data consistency of the entire quality inspection process, and provides reliable data support for subsequent quality anomaly identification, analysis, and early warning.

[0050] For example, in the data acquisition sub-interface 200, the quality control administrator first clicks the inspection item configuration control 203, adds the "glass surface flatness" inspection category and corresponding inspection items such as "bubble diameter" and "scratch length" in the inspection item configuration window, and deletes redundant inspection items such as "edge perpendicularity" that are no longer needed; then clicks the "defect comparison table" control, adds the "stress spot" defect type in the defect details, and changes the judgment standard for "stone" defects from "diameter > 0.5mm" to "diameter > 0.3mm". After completing the configuration, click save, and the system automatically synchronizes the updated inspection item configuration and defect comparison table to the associated inspection equipment to ensure that the data entry fields are consistent with the equipment inspection parameters.

[0051] In some embodiments of this application, the quality anomaly tracking sub-interface includes an anomaly event initiation control and a quality anomaly analysis control. In step S130, in the quality anomaly tracking sub-interface, a quality anomaly event is initiated based on the basic quality inspection data, and defect isolation, recall, tracing, and tracking operations are performed to generate an anomaly handling record. The process of generating a quality anomaly analysis report includes, but is not limited to, the following steps: defect type statistics, defect cause root cause analysis, and inspection equipment data analysis.

[0052] Step S410: In response to the trigger command of the abnormal event initiation control, the abnormal event initiation window is displayed, and a quality abnormal event is initiated based on the quality inspection basic data. Defect isolation, recall, tracing and tracking operations are performed to generate an abnormal handling record.

[0053] The exception handling records include isolation tray information, recall execution logs, traceability detection results, and tracking progress information.

[0054] In step S410, in the quality anomaly tracking sub-interface, the user clicks the anomaly event initiation control. The system then pops up an anomaly event initiation window, guiding the user to quickly locate abnormal data points based on previously entered quality inspection data (such as original melt composition, thermal testing parameters, and test results), and initiate the corresponding quality anomaly event. This process not only enables timely identification of quality problems but also guides the user to perform a series of standardized processing operations, including isolation of defective products (such as recording the isolation tray number, location, and quantity), recall (recording the recall time, responsible person, and processing method), traceability (associating the source of raw materials, production equipment, and operators of the defective batch), and tracking (recording processing progress, processing results, and verification status).

[0055] Ultimately, the system automatically generates structured exception handling records, covering isolation tray information, recall execution logs, traceability detection results, and tracking progress information, providing detailed data support for subsequent quality analysis, accountability, and process optimization. This step effectively improves the standardization and efficiency of exception handling, avoiding human error or incomplete processing.

[0056] Step S420: In response to the trigger command of the quality anomaly analysis control, the quality anomaly analysis window is displayed. Based on the anomaly handling records and basic quality inspection data, defect type statistics, defect cause root cause analysis, and inspection equipment data analysis are performed to generate a quality anomaly analysis report.

[0057] The quality anomaly analysis report includes a defect distribution trend chart, a defect root cause map, and equipment rejection rate data.

[0058] In step S420, after the user clicks the quality anomaly analysis control, the system will pop up a quality anomaly analysis window. Based on the previously generated anomaly handling records and the original quality inspection data, the system will automatically perform multi-dimensional data analysis. First, the system will classify and statistically analyze historical and current defect types, generating a defect distribution trend chart to help users identify high-frequency defects and their changing trends. Second, the system will conduct root cause analysis of defects by linking defect data with production process data (such as raw material batches, equipment parameters, process settings, etc.), forming a visualized defect root cause map to reveal potential root causes, such as equipment aging, raw material fluctuations, or unstable process parameters. Finally, the system will also analyze the operating performance of the testing equipment, evaluate the efficiency of different equipment in anomaly identification and rejection, and output equipment rejection rate data to provide a basis for equipment maintenance and optimization.

[0059] Ultimately, the system integrates these analysis results into a structured quality anomaly analysis report, which quality management personnel can review, archive, or use as a reference for subsequent improvement measures. This step not only achieves closed-loop management of quality issues from discovery to analysis to improvement, but also enhances the scientific rigor and accuracy of quality decisions through a data-driven approach, providing strong technical support for improving product quality and production stability.

[0060] In some embodiments of this application, reference is made to Figure 4 The exception event initiation window 300 includes a defect isolation control 301, a defect recall control 302, a defect tracing control 303, a defect tracking control 304, and an exception handling record saving control 305. In step S410, the process of initiating a quality exception event based on quality inspection basic data in the exception event initiation window 300, performing defect isolation, recall, tracing, and tracking operations to generate an exception handling record includes, but is not limited to, the following steps.

[0061] In step S510, in response to the trigger command of the defect isolation control 301, the defect isolation window is displayed, the isolation pallet information is set, the target work order is automatically associated, and an isolation notification is generated and pushed to the warehouse management system.

[0062] The isolation notification includes a description of the defect, the number of isolations, the severity of the defect, and the location information of the target warehouse.

[0063] In step S510, isolation measures are implemented to prevent the further spread of problematic products. When the user triggers the defect isolation control 301, a defect isolation window pops up, allowing the user to record detailed information about the products to be isolated, including specific pallet numbers, locations, and other details. Furthermore, the system automatically associates these isolation operations with the corresponding target work orders, ensuring that each batch of problematic products can be accurately tracked and managed. Next, the system automatically generates an isolation notification form containing a defect description, isolation quantity, defect severity, and target warehouse location information, and pushes it to the warehouse management system. This step not only helps to quickly identify problematic products but also ensures that the warehouse department can take timely action to prevent non-conforming products from mixing with qualified product inventory, thereby guaranteeing product quality safety and traceability.

[0064] In step S520, in response to the trigger command of the defect recall control 302, the defect recall window is displayed and a recall execution log is generated.

[0065] The recall execution log is used to record the batch, quantity, and processing progress of the recalled products.

[0066] In step S520, when the user triggers the defect recall control 302, the system displays a defect recall window, guiding the user to initiate the recall process. During this process, the system automatically generates a detailed recall execution log, recording all recall-related information, such as the product batches involved, specific quantities, and processing progress. This detailed logging provides a complete chain of evidence for subsequent review, ensuring that every recall operation is traceable. Simultaneously, it provides management with a clear view to monitor the progress of the recall work, ensuring that all affected products are effectively handled, minimizing potential risks and losses.

[0067] In step S530, in response to the trigger command of the defect tracing control 303, the defect tracing window is displayed and the tracing detection results are recorded.

[0068] Among them, the traceability detection results are used to locate the link in which the anomaly occurred and the scope of its impact.

[0069] In step S530, once the user triggers the defect tracing control 303, the system will display a defect tracing window, where the user can input relevant tracing and detection results. This data may include the source of raw materials, the status of production equipment, and production process parameters, used to analyze the specific stage where the anomaly occurred and the range of affected products. Through this method, companies can not only accurately identify the source of the problem but also assess its impact on the entire production chain, thereby developing targeted improvement measures. This process is crucial for improving product quality control because it helps companies solve problems at their root and prevent similar problems from recurring in the future.

[0070] In step S540, in response to the trigger command of the defect tracking control 304, the defect tracking window is displayed and the tracking progress information is updated.

[0071] The progress tracking information is used to monitor the nodes and completion status of the exception handling process.

[0072] In step S540, when the user triggers the defect tracking control 304, the system displays a defect tracking window. Users can update various progress information regarding the anomaly handling process in real time on this interface. This information covers the status and completion status of each processing node, such as whether a preliminary investigation has been completed and whether corresponding corrective measures are being implemented. By continuously updating the progress information, managers can understand the processing progress at any time, adjust strategies or resource allocation in a timely manner, and ensure that anomalies are resolved efficiently. Furthermore, a transparent tracking mechanism also helps enhance team collaboration efficiency, ensuring that each participant is clear about the current tasks and objectives.

[0073] In step S550, in response to the trigger command of the exception handling record saving control 305, the isolation tray information, recall execution log, trace detection results and tracking progress information are saved, and an exception handling record is generated.

[0074] In step S550, after the user confirms that all necessary information has been entered correctly, they click the exception handling record save control 305. The system will then summarize and save the isolation tray information, recall execution log, traceability test results, and tracking progress information, forming a complete exception handling record. This record not only provides a comprehensive review of the handling process of this exception event but also offers valuable experience for similar events in the future. More importantly, it provides detailed documentation support for internal audits and external regulatory inspections, demonstrating that the company has taken proactive and effective measures when facing quality issues. Furthermore, the accumulated exception handling records can be used for data analysis to help identify common quality problems and their root causes, thereby driving continuous improvement.

[0075] In some embodiments of this application, the specific process of initiating a quality anomaly event based on the quality inspection basic data in the anomaly event initiation window 300, performing defect isolation, recall, tracing and tracking operations, and generating an anomaly handling record is exemplified below.

[0076] After the quality inspector discovers a "cracked bottle" defect in a batch of glass bottles in the abnormal event initiation window 300 (based on the judgment result of "crack length > 2mm" in the basic quality inspection data), the defect isolation control 301 is triggered first. The system displays the defect isolation window, the quality inspector selects work order "G2023102001" and checks the isolation pallet numbers "TP001-TP005", sets the defect severity to "severe", and the system automatically associates the work order with the corresponding storage area "B Zone 3", generates an isolation notification containing "longitudinal crack in the bottle body, involving 5 pallets with a total of 2000 products, which need to be urgently isolated to the inspection area", and pushes it to the warehouse management system; then the defect recall control 302 is triggered, and the downstream warehouse pallet "WH012-015" that has been shipped is selected in the recall window. The system generates a recall execution log, recording "Recall batch G202310 2001, Quantity 800 pieces, Current progress: The logistics department has been notified to arrange for return; then the defect traceability control 303 is triggered, and the original melt detection data (such as "kiln temperature fluctuation ±5℃ on October 19") and molding process parameters are entered in the traceability window, generating the traceability detection result "The anomaly occurred due to the temperature runaway in the original melting process, affecting 3 batches produced between 14:00 and 16:00 on October 19"; then the defect tracking control 304 is triggered, and the progress information is updated in the tracking window "Isolation re-inspection completed at 10:00 on October 20, and the original melting department adjusted the temperature control parameters at 15:00 on October 20"; finally, the anomaly handling record save control 305 is clicked, and the system automatically summarizes the isolation pallet information, recall execution log, traceability detection results and tracking progress information, generating an anomaly handling record with the number "QA-20231020-001" and archiving it.

[0077] In some embodiments of this application, reference is made to Figure 5 The quality anomaly analysis window 400 includes a defect statistics analysis control 401, an equipment data analysis control 402, and a report generation control 403. In step S420, within the quality anomaly analysis window 400, based on anomaly handling records and basic quality inspection data, the process of generating a quality anomaly analysis report by performing defect type statistics, defect cause root cause analysis, and inspection equipment data analysis includes, but is not limited to, the following steps.

[0078] In step S610, in response to the trigger command of the defect statistical analysis control 401, the defect statistical analysis window is displayed, defect type frequency statistics and cause correlation analysis are performed, and defect distribution trend map and defect root cause map are generated.

[0079] Among them, the defect distribution trend map is used to show the frequency change trend of various defects in different periods, and the defect root cause map is used to locate the key process or material factors that cause defects.

[0080] In step S610, statistical analysis of historical and current defect data reveals the distribution patterns and potential causes of quality defects. When the user triggers the defect statistical analysis control 401, the system will pop up a defect statistical analysis window. The user can select filtering conditions such as statistical period, defect type dimension, and sample range on this interface. Based on anomaly handling records and basic quality inspection data, the system automatically performs frequency statistics of defect types and correlation analysis of defect occurrence causes.

[0081] The final step generates a defect distribution trend map, which visually displays the frequency changes of various defects (such as bubbles, cracks, impurities, deformation, etc.) over different time periods, helping companies identify patterns in quality fluctuations. Simultaneously, a defect root cause map is generated, which uses multi-dimensional correlation analysis with defect data and information on processes, equipment, raw materials, and operators to accurately pinpoint key processes or material factors leading to defects. This step provides quality management personnel with a scientific and visual analytical tool, facilitating a comprehensive understanding from macro trends to micro causes, and providing data support for subsequent process optimization and quality improvement.

[0082] In step S620, in response to the trigger command of the equipment data analysis control 402, the equipment data analysis window is displayed, and the correlation analysis and processing of equipment operating parameters and rejection data are performed to obtain equipment rejection rate data.

[0083] Among them, the equipment rejection rate data is used to evaluate the accuracy and anomaly identification capabilities of the testing equipment.

[0084] In step S620, data analysis is used to improve the equipment's efficiency and anomaly identification capabilities. When the user clicks the equipment data analysis control 402, the system enters the equipment data analysis window. In this interface, the system combines basic quality inspection data, anomaly handling records, and equipment operation logs to perform a correlation analysis on the parameters of the testing equipment during actual operation (such as detection speed, sensitivity, identification threshold, etc.) and the rejection data (i.e., the number of non-conforming products identified by the equipment).

[0085] Through this analysis, the system can calculate the equipment rejection rate, which is the ratio of the number of abnormal products successfully identified and rejected by the equipment within a specific time period to the total number of products inspected. This data can not only be used to evaluate the equipment's inspection accuracy and stability but also help identify problems of false rejections or missed inspections, thus providing a basis for equipment calibration, maintenance, and optimization. Furthermore, by comparing the rejection rates of multiple machines, it can also assist companies in optimizing equipment layout and inspection strategies, improving overall inspection efficiency and quality control levels.

[0086] In step S630, in response to the trigger command of the report generation control 403, the abnormal handling record, defect distribution trend map, defect root cause map and equipment rejection rate data are associated to generate a quality abnormality analysis report.

[0087] In step S630, the results of the preceding analysis are integrated into a well-structured, detailed, and easily accessible quality anomaly analysis report. When the user triggers the report generation control 403, the system automatically associates previously generated anomaly handling records (including isolation, recall, tracing, and tracking operation records), defect distribution trend charts, defect root cause maps, and equipment rejection rate data, integrating and formatting them according to a preset report template to ultimately generate a complete quality anomaly analysis report. This report not only covers the handling process of the anomaly event but also visually presents the defect type distribution, root cause analysis, and equipment operating performance through charts and graphs, facilitating a comprehensive understanding of the problem by quality management personnel, technical teams, and management, enabling them to develop targeted improvement measures.

[0088] Furthermore, this report can serve as a basis for internal quality improvement or for quality traceability and auditing by external clients and regulatory agencies. The implementation of this step effectively achieves closed-loop management throughout the entire process, from data collection and anomaly handling to in-depth analysis and output, providing strong system support for the company's quality improvement and continuous development.

[0089] In some embodiments of this application, the specific process of generating a quality anomaly analysis report by performing defect type statistics, defect cause root cause analysis, and detection equipment data analysis based on anomaly handling records and basic quality inspection data in the quality anomaly analysis window 400 is illustrated below.

[0090] In the quality anomaly analysis window 400, the quality control administrator first triggers the defect statistics analysis control 401. The system displays a statistics window and automatically loads the basic quality inspection data for October 2023. Through frequency statistics, "bubbles" (32 times) and "cracks" (28 times) are found to be the main defect types. After cause correlation analysis, a defect distribution trend chart (showing a 40% surge in bubble defects in the second week of October) and a defect root cause map are generated (identifying the key factors as "fluctuations in the original melt temperature" and "unstable pressure of the kiln and column presses") are generated. Then, the equipment data analysis control 402 is triggered. In the equipment data analysis window, kiln 1 and row presses are selected. For machine 3, the system automatically associates the operating parameters and rejection data of the past 30 days to calculate the equipment rejection rate (e.g., the rejection rate of machine 3 is 8.2%, which is 3.5% higher than the benchmark value) and evaluates its anomaly identification accuracy. Finally, the report generation control 403 is triggered, and the system automatically associates the anomaly handling record with the number "QA-20231020-001", defect distribution trend chart, root cause map and equipment rejection rate data to generate a quality anomaly analysis report including three parts: "October defect type percentage", "key process anomaly point location" and "equipment performance evaluation", and supports exporting to PDF format for archiving.

[0091] In some embodiments of this application, the defect statistical analysis window includes a data filtering control, a defect trend chart generation control, and a defect root cause map generation control. In step S610, the process of performing defect type frequency statistics and cause correlation analysis in the defect statistical analysis window to generate a defect distribution trend chart and a defect root cause map includes, but is not limited to, the following steps.

[0092] In step S710, in response to the trigger command of the data filtering control, the data filtering window is displayed, and the statistical period, defect type dimension and sample range parameters are selected as data filtering conditions.

[0093] In step S710, within the defect statistical analysis window, the user clicks the data filtering control, and the system pops up a data filtering window where the user can customize the data range required for analysis. For example, the user can select the statistical period (such as the past week, the past month, or a specific date range) to observe quality fluctuations over different time periods; the user can also select the defect type dimension (such as bubbles, cracks, inclusions, etc.) to focus on the distribution pattern of a certain type of defect; and the user can also set sample range parameters (such as a specific production line, a specific work order, or a specific batch) to conduct in-depth analysis of the quality performance of a specific production unit. This flexible data filtering mechanism ensures the relevance and accuracy of the analysis results, avoids interference from irrelevant data, improves analysis efficiency, and provides a high-quality data foundation for subsequent chart generation and defect root cause analysis.

[0094] Step S720: In response to the trigger command of the defect trend chart generation control, the defect trend chart generation window is displayed, the time series granularity, data aggregation method and abnormal threshold marking rules are set, and a defect distribution trend chart is generated in combination with the data filtering conditions.

[0095] In step S720, the distribution pattern of defects over time is visually displayed in chart form, helping users quickly identify abnormal quality fluctuation trends. When the user clicks the defect trend chart generation control, the system enters the defect trend chart generation window. In this interface, the user can further configure chart generation parameters: set the time series granularity (e.g., by hour, day, week) to adapt to trend analysis needs under different production rhythms; select the data aggregation method (e.g., count, sum, average, etc.) to determine how to count the number of defects; and set anomaly threshold marking rules (e.g., setting a red warning if the number of a certain type of defect exceeds a certain value) to automatically identify abnormal fluctuation points in the chart. The system will combine the data filtering conditions set by the user in S710 to automatically generate a structured defect distribution trend chart, clearly showing the frequency changes of various types of defects over different time periods. This chart not only helps identify periodic quality problems but also provides data for evaluating the effectiveness of quality improvement measures, making it an important tool for enterprises to conduct quality monitoring and trend early warning. In step S730, in response to the trigger command of the defect root-seeking map generation control, the root-seeking analysis window is displayed, the root-seeking analysis algorithm is executed, and the defect root-seeking map is generated and displayed.

[0096] In step S730, a root cause analysis algorithm is used to perform multi-dimensional correlation analysis between defect data and production process data, thereby revealing the key influencing factors that lead to defects. When the user triggers the defect root cause map generation control, the system pops up the root cause analysis window and automatically executes the preset root cause analysis algorithm. This algorithm is based on multi-source data such as defect data, anomaly handling records, detection equipment data, process parameters, and raw material batches. Through correlation analysis, causal reasoning, or machine learning models, it identifies process nodes, equipment parameters, or material batches that are highly correlated with the occurrence of defects.

[0097] The system ultimately generates and displays a defect root cause map, visually presenting the correlation between defects and influencing factors. For example, the map might show a high correlation between a certain type of crack defect and the cooling parameter settings of a particular molding equipment, or a certain type of bubble defect might be closely related to the melting temperature fluctuations of a specific batch of raw materials. This map provides enterprises with scientific and intuitive decision support, helping quality management personnel accurately pinpoint the root causes of problems and formulate targeted process optimization, equipment maintenance, or raw material control measures. This effectively improves product quality stability and promotes the transformation of quality management from post-processing to pre-prevention.

[0098] In some embodiments of this application, reference is made to Figure 6 The quality risk early warning sub-interface 500 includes a risk early warning threshold setting control 501 and an early warning information push control 502. In step S140, the process of pushing production line quality risk early warning information and displaying corresponding abnormality handling records and quality abnormality analysis reports in the quality risk early warning sub-interface 500 based on basic quality inspection data and preset risk thresholds includes, but is not limited to, the following steps.

[0099] In step S810, in response to the trigger command of the risk warning threshold setting control 501, the threshold configuration window is displayed to set the defect rate threshold, equipment parameter deviation threshold and raw material fluctuation threshold.

[0100] In step S810, various early warning thresholds are flexibly configured to achieve dynamic identification and hierarchical management of potential quality risks. In the quality risk early warning sub-interface 500, the user clicks the risk early warning threshold setting control 501, and the system pops up a threshold configuration window, allowing the user to set risk early warning thresholds for multiple dimensions based on actual production needs and historical quality data. These include: a defect rate threshold, used to monitor whether the number of product defects per unit time exceeds an acceptable range; an equipment parameter deviation threshold, used to determine whether the operating parameters of key testing or production equipment deviate from standard values, thereby predicting the risk of equipment malfunction or testing failure; and a raw material fluctuation threshold, used to identify whether there are abnormal fluctuations in the composition or performance indicators of raw materials, which may affect product quality stability. Through the implementation of this step, the system can flexibly adjust the early warning sensitivity according to different production scenarios and product types, ensuring that the early warning mechanism is neither too sensitive, leading to frequent false alarms, nor too lenient, causing missed major risks, thus providing a scientific basis for subsequent early warning pushes and risk control.

[0101] In step S820, in response to the trigger command of the early warning information push control 502, an early warning push window is displayed, and based on the basic data of quality inspection and the preset risk threshold, production line quality risk early warning information is generated, and the corresponding abnormal handling records and quality abnormality analysis reports are displayed in conjunction with the information and sent to the designated terminal.

[0102] In step S820, a systematic early warning generation and push mechanism is used to achieve rapid response and closed-loop management of potential quality problems. When the user clicks the early warning information push control 502, the system enters the early warning push window. The system automatically compares and analyzes the currently collected basic quality inspection data (such as original melt composition, thermal inspection parameters, test results, etc.) with the various risk thresholds configured in step S810 to identify whether there are abnormal trends or potential risks.

[0103] Once a certain indicator reaches or exceeds a preset threshold, the system generates a corresponding production line quality risk warning and displays related anomaly handling records (such as defect isolation, recall, and tracing operation records) and quality anomaly analysis reports (including defect distribution trend charts, defect root cause maps, and equipment rejection rate data), helping users quickly understand the handling and analysis conclusions of similar historical issues. Subsequently, the system pushes the warning information to designated terminals of relevant responsible persons (such as quality management personnel, production line supervisors, and equipment maintenance personnel) via the company's internal communication system or mobile devices, ensuring that relevant personnel can intervene immediately to prevent the risk from escalating. This step not only improves the company's response speed and handling efficiency to quality risks but also achieves closed-loop management of the entire process from "early warning to analysis to handling to feedback," providing technical support for comprehensive quality management that includes "pre-warning, in-process control, and post-improvement."

[0104] In some embodiments of this application, the specific process of pushing production line quality risk warning information and displaying corresponding abnormality handling records and quality abnormality analysis reports in the quality risk warning sub-interface 500 based on basic quality inspection data and preset risk thresholds is illustrated below.

[0105] In the quality risk warning sub-interface 500, the quality inspection administrator first triggers the risk warning threshold setting control 501. The system displays the threshold configuration window, where the administrator sets the defect rate threshold to "2%", the equipment parameter deviation threshold to "±5%" (such as the pressure of the lathe), and the raw material fluctuation threshold to "±3%" (such as the purity of quartz sand), and saves the configuration. Then, the system triggers the warning information push control 502. The system compares the basic quality inspection data in real time in the warning push window and finds that the daily defect rate of "kiln 2" on the production line reaches 3.5% (exceeding the threshold). The system immediately generates the production line quality risk warning information "The bubble defect rate of kiln 2 is 3.5%, which exceeds the threshold of 2% and requires emergency handling". At the same time, the corresponding abnormal handling record (number "QA-20231020-001") and the quality abnormality analysis report (including the defect root cause map "original melting temperature fluctuation") are displayed. The warning information and related documents are automatically pushed to the production supervisor's mobile terminal and the workshop large screen terminal.

[0106] In summary, the glass product quality anomaly tracking and management interactive method provided in this application has the following technical effects.

[0107] This method solves the problem of information silos through an integrated platform and unified interface. Users can access all relevant quality management tools and information on a single interface, improving work efficiency and ensuring data consistency and integrity. The method enables real-time input and analysis of raw melt testing, thermal testing, and trial run data, generating basic quality inspection data. This helps to promptly identify potential quality problems and provides reliable data support for subsequent control. The system has a rapid response mechanism, capable of quickly initiating quality anomaly events, performing defect isolation, recall, tracing, and tracking operations to ensure efficient handling of quality problems. Root cause analysis is achieved through detailed anomaly handling records.

[0108] Furthermore, the system boasts powerful traceability capabilities and a comprehensive early warning system. Based on detailed data analysis, it can identify key processes or material factors leading to defects and automatically push production line quality risk warnings when preset risk thresholds are reached, helping companies take preventative measures in advance. Deep data analysis capabilities support accurate understanding of the frequency trends of various defects across different cycles, assess the rejection rate data of inspection equipment, and optimize production processes through flexible data filtering and visualization tools. The entire process forms a closed-loop management system from data acquisition, anomaly handling, in-depth analysis to output results. It not only achieves full-process management of "early warning—analysis—processing—feedback," but also provides companies with scientific and intuitive decision-making basis, promoting the shift from "post-event handling" to "pre-event prevention," significantly improving the overall effectiveness of quality management, ensuring that quality problems are resolved promptly and effectively, and providing strong support for companies to achieve high-quality production goals.

[0109] Secondly, embodiments of this application provide a glass product quality anomaly tracking and management interactive system for executing the aforementioned glass product quality anomaly tracking and management interactive method. (Refer to...) Figure 7 The system includes a main interface module 910, a data acquisition module 920, a quality anomaly tracking module 930, and a quality risk early warning module 940.

[0110] The main interface module 910 is used to display the main interface for tracking and managing quality anomalies in glass products; the main interface includes data acquisition controls, quality anomaly tracking controls, and quality risk warning controls. The data acquisition module 920 is used to respond to the trigger command of the data acquisition control, display the data acquisition sub-interface, and enter the original melt test data, heat test data and test data to generate basic data for quality inspection; The quality anomaly tracking module 930 is used to respond to the trigger command of the quality anomaly tracking control, display the quality anomaly tracking sub-interface, initiate quality anomaly events based on the basic data of quality inspection, perform defect isolation, recall, tracing and tracking operations, generate anomaly handling records, and perform defect type statistics, defect cause root cause analysis and inspection equipment data analysis to generate a quality anomaly analysis report. The quality risk early warning module 940 is used to respond to the trigger command of the quality risk early warning control, display the quality risk early warning sub-interface, push production line quality risk early warning information based on the basic data of quality inspection and the preset risk threshold, and display the corresponding abnormal handling records and quality abnormality analysis reports in conjunction with the display.

[0111] Furthermore, embodiments of this application provide a computer-readable storage medium storing a processor-executable program, which, when executed by a processor, is used to implement the aforementioned glass product quality anomaly tracking and management interactive method.

[0112] Similarly, the systems and media provided in this application have the same technical effects as those in the above method embodiments, and will not be described again here.

[0113] In some alternative embodiments, the functions / operations mentioned in the block diagrams may not occur in the order shown in the operation diagrams. For example, depending on the functions / operations involved, two consecutively shown blocks may actually be executed substantially simultaneously, or the blocks may sometimes be executed in reverse order. Furthermore, the embodiments presented and described in the flowcharts of this application are provided by way of example to provide a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and sub-operations described as part of a larger operation are executed independently.

[0114] Furthermore, although this application is described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding this application. Rather, given the properties, functions, and internal relationships of the various functional modules in the apparatus disclosed herein, the actual implementation of the module will be understood within the scope of ordinary skill of an engineer. Therefore, those skilled in the art can implement the application set forth in the claims using ordinary skill. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of this application, which is determined by the full scope of the appended claims and their equivalents.

[0115] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several programs to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0116] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequential list of executable programs for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, a program execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can retrieve and execute a program from or in conjunction with such a program execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can mean any means that can contain, store, communicate, propagate, or transmit a program for use by or in conjunction with a program execution system, apparatus, or device.

[0117] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or, if necessary, processing in a suitable manner, and then stored in computer memory.

[0118] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable program execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0119] In the foregoing description of this specification, the reference to terms such as "one embodiment / implementation," "another embodiment / implementation," or "certain embodiments / implementations," etc., indicates that a specific feature, structure, material, or characteristic described in connection with an embodiment or example is included in an embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0120] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

[0121] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of the present invention.

Claims

1. A method for tracking and managing quality anomalies in glass products, characterized in that, Includes the following steps: The main interface for tracking and managing quality anomalies in glass products is displayed; the main interface includes data acquisition controls, quality anomaly tracking controls, and quality risk warning controls. In response to the trigger command of the data acquisition control, the data acquisition sub-interface is displayed to enter the original melt test data, thermal test data and test data to generate basic quality test data; In response to the trigger command of the quality anomaly tracking control, the quality anomaly tracking sub-interface is displayed, a quality anomaly event is initiated based on the quality inspection basic data, defect isolation, recall, tracing and tracking operations are performed to generate anomaly handling records, and defect type statistics, defect cause root cause analysis and inspection equipment data analysis are performed to generate a quality anomaly analysis report. In response to the trigger command of the quality risk warning control, the quality risk warning sub-interface is displayed. Based on the basic quality inspection data and the preset risk threshold, the production line quality risk warning information is pushed, and the corresponding abnormal handling records and quality abnormality analysis reports are displayed in conjunction.

2. The glass product quality anomaly tracking and management interactive method according to claim 1, characterized in that, The data acquisition sub-interface includes a work order selection control and a test data entry control; In the data acquisition sub-interface, the original melt test data, thermal test data, and trial run data are entered to generate basic quality inspection data, including the following steps: In response to the trigger command of the work order selection control, a work order selection window is displayed, and the target work order for which test data is to be entered is selected; In response to the trigger command of the detection data entry control, the detection data entry window is displayed, and the original melt composition data, thermal test parameter data and test result data of the target work order are entered to generate the basic quality inspection data of the target work order.

3. The glass product quality anomaly tracking and management interactive method according to claim 2, characterized in that, The data acquisition sub-interface also includes a test item configuration control; in the data acquisition sub-interface, you can configure custom test items and defect comparison tables, add defect types, modify judgment criteria, and delete redundant test items, and synchronize the configuration results to the testing equipment, including the following steps: In response to the trigger command of the inspection item configuration control, the inspection item configuration window is displayed, allowing for the addition, modification, or deletion of custom inspection items, and maintaining a table of defect types and judgment criteria; In response to the configuration completion command, the updated inspection item configuration and defect comparison table are synchronized to the associated inspection equipment to ensure that the data entry fields are consistent with the equipment inspection parameters.

4. The glass product quality anomaly tracking and management interactive method according to claim 1, characterized in that, The quality anomaly tracking sub-interface includes an anomaly event initiation control and a quality anomaly analysis control; In the quality anomaly tracking sub-interface, a quality anomaly event is initiated based on the quality inspection basic data. Defect isolation, recall, tracing, and tracking operations are performed to generate anomaly handling records. Furthermore, defect type statistics, defect cause analysis, and inspection equipment data analysis are conducted to generate a quality anomaly analysis report, including the following steps: In response to the trigger command of the abnormal event initiation control, an abnormal event initiation window is displayed, and the quality abnormal event is initiated based on the quality inspection basic data to perform defect isolation, recall, tracing and tracking operations to generate the abnormal handling record; The exception handling record includes isolation tray information, recall execution log, traceability detection results, and tracking progress information; In response to the trigger command of the quality anomaly analysis control, the quality anomaly analysis window is displayed. Based on the anomaly handling record and the quality inspection basic data, defect type statistics, defect cause root cause analysis and inspection equipment data analysis are performed to generate the quality anomaly analysis report. The quality anomaly analysis report includes a defect distribution trend chart, a defect root cause map, and equipment rejection rate data.

5. The glass product quality anomaly tracking and management interactive method according to claim 4, characterized in that, The abnormal event initiation window includes a defect isolation control, a defect recall control, a defect tracing control, a defect tracking control, and an abnormal handling record saving control; In the abnormal event initiation window, the quality abnormal event is initiated based on the quality inspection basic data, and defect isolation, recall, tracing and tracking operations are performed to generate the abnormal handling record, including the following steps: In response to the trigger command of the defect isolation control, a defect isolation window is displayed, isolation pallet information is set, target work orders are automatically associated, and an isolation notification is generated and pushed to the warehouse management system; wherein, the isolation notification includes a defect description, isolation quantity, defect severity, and target warehouse location information; In response to the trigger command of the defect recall control, a defect recall window is displayed and a recall execution log is generated; the recall execution log is used to record the batch, quantity and processing progress of the recalled products. In response to the trigger command of the defect tracing control, a defect tracing window is displayed, and the tracing detection results are recorded; the tracing detection results are used to locate the abnormality generation process and its scope of influence. In response to a trigger command on the defect tracking control, a defect tracking window is displayed, and the tracking progress information is updated; the tracking progress information is used to monitor the exception handling process nodes and completion status. In response to a trigger command to the exception handling record saving control, the isolation tray information, the recall execution log, the trace detection result, and the tracking progress information are saved, and the exception handling record is generated.

6. The glass product quality anomaly tracking and management interactive method according to claim 4, characterized in that, The quality anomaly analysis window includes defect statistical analysis controls, equipment data analysis controls, and report generation controls; In the quality anomaly analysis window, based on the anomaly handling records and the basic quality inspection data, defect type statistics, defect cause root cause analysis, and inspection equipment data analysis are performed to generate the quality anomaly analysis report, including the following steps: In response to the trigger command of the defect statistical analysis control, the defect statistical analysis window is displayed, defect type frequency statistics and cause correlation analysis are performed, and defect distribution trend map and defect root cause map are generated. The defect distribution trend map is used to show the frequency change trend of various types of defects in different periods, and the defect root-finding map is used to locate the key process or material factors that cause defects. In response to the trigger command of the device data analysis control, the device data analysis window is displayed to perform correlation analysis and processing between device operating parameters and rejection data, and to obtain device rejection rate data; The equipment rejection rate data is used to evaluate the accuracy and anomaly identification capabilities of the detection equipment. In response to a trigger command on the report generation control, the anomaly handling record, the defect distribution trend map, the defect root cause map, and the equipment rejection rate data are associated to generate the quality anomaly analysis report.

7. The glass product quality anomaly tracking and management interactive method according to claim 6, characterized in that, The defect statistical analysis window includes a data filtering control, a defect trend chart generation control, and a defect root cause map generation control. In the defect statistics and analysis window, defect type frequency statistics and cause correlation analysis are performed to generate defect distribution trend charts and defect root cause maps, including the following steps: In response to the trigger command of the data filtering control, a data filtering window is displayed, and the statistical period, defect type dimension and sample range parameters are selected as data filtering conditions; In response to the trigger command of the defect trend map generation control, the defect trend map generation window is displayed, the time series granularity, data aggregation method and abnormal threshold marking rules are set, and the defect distribution trend map is generated in combination with the data filtering conditions; In response to the trigger command of the defect root-seeking map generation control, the root-seeking analysis window is displayed, the root-seeking analysis algorithm is executed, and the defect root-seeking map is generated and displayed.

8. The glass product quality anomaly tracking and management interactive method according to claim 1, characterized in that, The quality risk early warning sub-interface includes a risk early warning threshold setting control and an early warning information push control; On the quality risk early warning sub-interface, based on the basic quality inspection data and preset risk thresholds, production line quality risk early warning information is pushed out, and corresponding anomaly handling records and quality anomaly analysis reports are displayed in conjunction, including the following steps: In response to the trigger command of the risk warning threshold setting control, a threshold configuration window is displayed to set the defect rate threshold, equipment parameter deviation threshold, and raw material fluctuation threshold; In response to the trigger command of the warning information push control, a warning push window is displayed. Based on the basic quality inspection data and the preset risk threshold, the production line quality risk warning information is generated, and the corresponding abnormality handling records and quality abnormality analysis reports are displayed in conjunction with the warning information push control and sent to the designated terminal.

9. A glass product quality anomaly tracking and management interactive system, characterized in that, Used to perform the glass product quality anomaly tracking and management interactive method as described in any one of claims 1 to 8.

10. A computer-readable storage medium storing a processor-executable program, characterized in that, The processor-executable program, when executed by the processor, is used to implement the glass product quality anomaly tracking and management interactive method as described in any one of claims 1 to 8.

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