Material flow tracing method and device for tray automated production line

Abstract

The present invention discloses a material flow tracing method and device for a tray automated production line, which relates to the field of manufacturing automation technology, including: configuring a smart label that is uniquely mapped to a raw material; performing label interaction of the smart label before executing any process unit to determine whether the process storage partition of the current process is activated; if activated, then performing processing data recording, and synchronously activating the smart sensor to perform material status monitoring after the current process is completed, and establishing material status monitoring results; synchronizing the material status monitoring results to the activated process storage partition in real time, and sending them to a central database to perform data analysis; and performing material flow tracing management of the tray automated production line according to the data analysis results and the smart label. The present invention solves the technical problem that it is difficult to quickly trace the root cause of the problem when production anomalies occur in the prior art, and achieves the technical effect of improving the efficiency of production line anomaly management and data analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of manufacturing automation, and in particular to a material flow tracing method and device for a tray automation production line. Background Art

[0002] In the tray automation production line, although the existing technology has the function of automated material flow management, it still has shortcomings in real-time material traceability and precise status monitoring. It is difficult to accurately and continuously record the processing data and status of materials during the production process. Especially when faced with complex multi-process flows, the process data lacks effective cross-step associations, resulting in difficulty in quickly tracing the root cause of production anomalies, affecting production efficiency and quality control. Summary of the invention

[0003] The present application provides a material flow tracing method and device for a tray automated production line, which is used to solve the technical problem in the prior art that it is difficult to quickly trace the root cause of a production anomaly when the production anomaly occurs.

[0004] In view of the above problems, the present application provides a material flow tracing method and device for an automated tray production line.

[0005] In a first aspect of the present application, a material flow tracing method for a tray automated production line is provided, the method comprising:

[0006] Before the raw materials are loaded, a smart label uniquely mapped to the raw materials is configured, wherein the smart label contains a unique identification ID and raw material data, and the smart label is configured with a preset process storage partition, and the process storage partition is activated through the pre-order partition; when the raw materials are circulated in the automated production line, before any process unit is executed, the label interaction of the smart label is performed to determine whether the process storage partition of the current process is activated; if the process storage partition of the current process is activated, the activated process storage partition is used to record the processing data, and after the current process processing is completed, the smart sensor on the process unit is synchronously activated to perform material status monitoring and establish material status monitoring results; the material status monitoring results are synchronized to the activated process storage partition in real time, and after the current process storage partition is encapsulated, it is sent to the central database to perform data analysis; the material flow traceability management of the tray automated production line is performed according to the data analysis results and the smart label.

[0007] The second aspect of the present application provides a material flow tracing device for a tray automated production line, the device comprising:

[0008] a label configuration module, which configures a smart label uniquely mapped to the raw material before the raw material is loaded, wherein the smart label contains a unique identification ID and raw material data, and the smart label is configured with a preset process storage partition, and the process storage partition is activated by the pre-order partition; a label interaction module, which performs label interaction of the smart label before any process unit is executed when the raw material is circulated in the automated production line, and determines whether the process storage partition of the current process is activated; a status monitoring module, which is used to use the activated process storage partition to record processing data if the process storage partition of the current process is activated, and after the current process is completed, synchronously activate the smart sensor on the process unit to perform material status monitoring and establish material status monitoring results; a data analysis module, which synchronizes the material status monitoring results to the activated process storage partition in real time, and sends the current process storage partition to the central database after packaging it to perform data analysis; a traceability management module, which performs material flow traceability management of the tray automated production line according to the data analysis results and the smart label.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] Before the raw materials are loaded, the present application configures a smart label that is uniquely mapped to the raw materials, wherein the smart label contains a unique identification ID and raw material data, and the smart label is configured with a preset process storage partition, and the process storage partition is activated through the pre-order partition; when the raw materials are circulated in an automated production line, before any process unit is executed, the label interaction of the smart label is performed to determine whether the process storage partition of the current process is activated; if the process storage partition of the current process is activated, the activated process storage partition is used to record the processing data, and after the current process processing is completed, the smart sensor on the process unit is synchronously activated to perform material status monitoring and establish material status monitoring results; the material status monitoring results are synchronized to the activated process storage partition in real time, and after the current process storage partition is encapsulated, it is sent to the central database to perform data analysis; and the material flow traceability management of the tray automated production line is performed based on the data analysis results and the smart label. The present invention solves the technical problem in the prior art that it is difficult to quickly trace the root cause of the problem when production anomalies occur. By configuring a uniquely mapped smart label for the raw materials before they are loaded, and activating the process storage partition of the label in each process step, accurate recording and real-time monitoring of the material status can be achieved. The data is synchronously monitored by smart sensors and updated to the central database in real time, ensuring that the entire process is traceable, thereby achieving the technical effect of improving the efficiency of production line anomaly management and data analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0012] Figure 1 A schematic diagram of a material flow tracing method for a tray automated production line provided in an embodiment of the present application;

[0013] Figure 2 A schematic diagram of the structure of a material flow tracing device for an automated tray production line provided in an embodiment of the present application.

[0014] Description of reference numerals: label configuration module 11 , label interaction module 12 , status monitoring module 13 , data analysis module 14 , traceability management module 15 . DETAILED DESCRIPTION

[0015] The present application provides a material flow tracing method and device for an automated production line for material trays, aiming to solve the technical problem in the prior art that it is difficult to quickly trace the root cause of a production anomaly. By configuring a uniquely mapped smart label for the raw materials before loading, and activating the process storage partition of the label in each process step, accurate recording and real-time monitoring of the material status are achieved. The data is synchronously monitored by smart sensors and updated to the central database in real time, ensuring that the entire process is traceable, thereby achieving the technical effect of improving the efficiency of production line anomaly management and data analysis.

[0016] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0017] It should be noted that any variations of the terms "include" and "have" are intended to cover non-exclusive inclusions. For example, a process, method, apparatus, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or inherent to these processes, methods, products or devices.

[0018] Embodiment 1, as Figure 1 As shown, the present application provides a material flow tracing method for a tray automated production line, the method comprising:

[0019] Step S100: Before the raw materials are loaded, a smart label uniquely mapped to the raw materials is configured, wherein the smart label contains a unique identification ID and raw material data, and the smart label is configured with a preset process storage partition, and the process storage partition is activated by a pre-order partition.

[0020] In the embodiment of the present application, a unique smart label is assigned to each batch of raw materials before the raw materials are loaded. The smart label is a digital label generated by virtual technology. The core of the smart label is a unique identification ID, which is assigned through a database generation algorithm to identify each specific batch of raw materials, ensuring that each batch of raw materials can be independently tracked in the production process. The generated smart label contains a unique identification ID and raw material data, where the raw material data includes the type of raw material, batch number, specifications, etc.

[0021] Process storage partitions are preset in the smart label, and each partition is used to record different process data of raw materials during the production process. These partitions are not activated all at once, but are activated in a pre-sequential partitioning order according to the process sequence in the production process. When the raw materials enter the automated production line, first determine whether the previous process step has been completed and ensure that its data has been correctly stored. If the verification is passed, the storage partition of the current process will be activated, allowing the relevant data in the processing process to continue to be recorded. For example, after the raw materials pass process A, the storage partition of process A will be activated for recording data. When process A is completed and stored, the partition of process B will be activated.

[0022] Through the above steps, the unique identification and complete process traceability path of raw materials are ensured when they enter the production line, and the accurate recording and management of the flow status and processing data of raw materials in different process units are achieved.

[0023] Step S200: When the raw materials are circulated in the automated production line, before any process unit is executed, the label interaction of the smart label is performed to determine whether the process storage partition of the current process is activated.

[0024] In the embodiment of the present application, when the raw materials are circulated in the automated production line, before the raw materials arrive at any process unit for execution, they communicate with the smart tags through standard industrial protocols. The smart tags are stored in the cloud database, and the current status of the batch of raw materials is identified by reading the unique identification ID in the tag.

[0025] After reading the process information of the smart label, the next step is to determine whether the process storage partition of the current process has been activated through the preceding order partition activation mechanism. Each process step corresponds to a process storage partition in the smart label. These partitions are not activated all at once, but are activated step by step according to the process sequence. First, check whether the storage partition of the previous process step has been activated and completed data recording. This verification process is implemented through database partition management technology. By querying the partition status of the previous process in the database, it is determined whether the partition has completely stored the data of the previous process.

[0026] If it is detected that the storage partition of the previous process has been activated and the data is complete, it is determined that the process storage partition of the current process is activated. If the storage partition of the previous process is not activated, it is determined that the process storage partition of the current process is not activated.

[0027] Furthermore, in the method provided in the embodiment of the application, the step of determining whether the process storage partition of the current process is activated further includes:

[0028] If the process storage partition of the current process is not activated, a self-check instruction is generated; based on the self-check instruction, a self-check of the storage data of the previous process storage partition is performed, and a self-check result is established; and material return management is performed according to the self-check result.

[0029] In an embodiment of the present application, when it is detected that the process storage partition of the current process is not activated, a self-check instruction is generated by automatic control logic.

[0030] Next, based on the self-check instruction, the storage data of the previous process storage partition is self-checked. The self-check process calls the data consistency verification technology in the database to conduct a comprehensive check on the storage data of the previous process step. Specifically, a verification algorithm, such as hash verification, is used to compare the results of data storage to check whether the data is missing, damaged, or transmitted incorrectly, to ensure that the previous process data is not missing or damaged. The storage data of the previous process storage partition includes process parameter data, which is collected and obtained through preset sensors.

[0031] After the self-inspection is completed, the generated self-inspection results will be used to decide whether to perform material return management. If the self-inspection results show that the data is incomplete or abnormal, the material return mechanism will be triggered and the material will be returned to the previous process step for reprocessing or data correction.

[0032] Furthermore, in the method provided in the embodiment of the application, the material return management according to the self-inspection result further includes:

[0033] Establish redundant sensors, which are used to perform verification data collection of materials; when materials are returned for management, self-inspection abnormal value judgment is performed based on the self-inspection results; if the self-inspection abnormal value meets the preset threshold, the redundant sensor is activated to re-collect the material data, and the material return management is completed according to the re-collection results.

[0034] In the embodiment of the present application, redundant sensors are configured at preset nodes in the production process. These sensors are used as backup devices to re-collect data. The redundant sensors include temperature sensors, pressure sensors, humidity sensors and optical sensors, which are used to comprehensively monitor the material status in production.

[0035] When a material enters the return management, it is processed in a certain process step. During this period, the self-inspection abnormal value is determined based on the self-inspection results. The self-inspection abnormal value determination is to compare the collected data with the pre-set process parameter threshold to determine whether there is an abnormal value. If some data exceeds the set threshold range, these data are determined as self-inspection abnormal values.

[0036] If the self-test abnormal value exceeds the preset threshold, after the material is reprocessed, the redundant sensor is activated to start re-collecting the material status data, such as temperature, pressure or humidity. If the re-collected data is within the preset threshold, it indicates that the material status is normal, confirming that the material return management process is completed.

[0037] Step S300: If the process storage partition of the current process is activated, the activated process storage partition is used to record processing data, and after the current process is completed, the intelligent sensor on the process unit is synchronously activated to perform material status monitoring and establish material status monitoring results.

[0038] In the embodiment of the present application, after the process storage partition is activated, a series of steps are performed to ensure accurate recording of processing data and effective monitoring of material status. Specifically, first, after the process storage partition is activated, the processing data of the current process, including important process parameters such as temperature, pressure, and speed, are recorded through the SCADA system, and the collected data is transferred to the previously activated storage partition for recording.

[0039] After the current process step is completed, the intelligent sensors on the process unit are activated to perform material status monitoring. Material status monitoring includes temperature sensors, pressure sensors, humidity sensors, etc. By performing material status monitoring, relevant material status data such as temperature changes, humidity, surface quality, etc. are obtained. The obtained data are summarized to obtain the material status monitoring results.

[0040] Step S400: synchronize the material status monitoring results to the activated process storage partition in real time, and package the current process storage partition and send it to the central database to perform data analysis.

[0041] In an embodiment of the present application, the material status monitoring results are synchronized in real time to the activated process storage partition. When all the material status monitoring results are synchronized, the current process storage partition is encapsulated. Encapsulation refers to integrating all data related to the process step, including processing data and material status monitoring data, into a complete file or data block. The encapsulated process storage partition is then sent to the central database to ensure that all process data can be centrally stored for subsequent unified management and analysis.

[0042] Finally, after the data is uploaded to the central database, data analysis begins.

[0043] Furthermore, in the method provided in the embodiment of the application, the current process storage partition is packaged and sent to the central database to perform data analysis, and the method further includes:

[0044] Utilize the central database to parse all process units, perform correlation analysis between process units, and establish process correlation results; after optimizing the central database using the process correlation results, read the processing data record results and material status monitoring results in the storage partition package; perform processing control analysis on the processing data record results to generate control analysis results; perform material status identification on the material status monitoring results to establish material status identification results; call backtracking data according to the process correlation results, perform status compensation for the material status identification results based on the backtracking data, and perform control execution evaluation based on the status compensation and control analysis results to establish control execution evaluation results; complete data analysis with the material status identification results and control execution evaluation results.

[0045] In an embodiment of the present application, the data of all process units are first parsed from a central database through SQL queries. The processing data, status monitoring data, and historical operation records of each process unit are stored in different tables. These data are extracted through SQL query statements, and the information of each process unit is loaded into the memory to prepare for subsequent analysis. The Apriori algorithm is then used to perform association analysis on the parsed process unit data. Specifically, processing parameters such as temperature, pressure, processing time, etc. are first extracted from each process unit. The Apriori algorithm is then used to analyze the frequent occurrence patterns of these parameters in multiple process units. Through multiple rounds of scanning and mining, the interdependencies between different process units are identified, frequent item sets are generated, and association rules between process units are established, and the established association rules between process units are used as process association results.

[0046] After the correlation analysis is completed, the central database is optimized based on the generated process correlation results. By adjusting the data partitions, the process unit data with strong correlation is placed in the same partition. This process uses partition storage technology to centrally store the data of the correlation unit, reduce cross-partition data queries, and thus improve the database reading efficiency. After the database optimization is completed, the processing data record results and material status monitoring results encapsulated in the storage partition are read through the partition index function.

[0047] Next, the processing data recording results are analyzed for processing control. The processing data recording results include key process parameters such as temperature, pressure, and processing speed collected during the production process. The statistical process control method is used to conduct an in-depth analysis of the processing data recording results. Specifically, by drawing control charts, such as X-bar control charts, the fluctuation range of these parameters is monitored, and the upper and lower control limits are set by technical experts to ensure that all parameters are within the preset control range. If a process parameter exceeds the control limit, mark the abnormal point, and record the time, location, and possible causes of these abnormalities. Through the control analysis of all process parameters, the control analysis results are generated. Among them, the cause of the abnormality is determined by correlation analysis and regression analysis. For example, if some process parameters are abnormal at the same time, check the operating status of related equipment, such as the working condition of the temperature controller or pressure sensor, to determine whether the abnormality is caused by equipment failure. By comparing historical data, if the same abnormality occurs repeatedly in the processing of different batches of raw materials, determine that the quality problem of the raw materials is the main cause of the abnormality.

[0048] Next, the material status monitoring results are used to identify the material status. These status data include the temperature, humidity, deformation, etc. of the material, which are collected in real time by sensors on the production line. These data are compared with the thresholds in the process standards, and the material status is judged using a threshold-based status classification method. By comparing the actual material status data with the process standards, it is identified whether the material status is "normal" or "abnormal". If the material status exceeds the set standard threshold, it is marked as abnormal, and the process steps, time and specific deviations of the abnormality are recorded in detail, and finally the material status identification results are generated.

[0049] After the material state identification result is generated, the backtracking data is called according to the process association result, and the state compensation is performed on the material state identification result. The process association result is the dependency relationship between process units obtained through the previous association analysis. Based on these association relationships, the historical data in the central database is called, which includes historical production records similar to the current process conditions. Through regression analysis, the current material state is compared with the material state changes under similar historical process conditions to find possible deviation trends. If historical data shows that when certain process parameters are close to a certain threshold, the material state often deviates, this historical data is used to compensate the current material state identification result. Specifically, state compensation corrects the errors in the current state identification by analyzing historical trends to ensure the accuracy of state identification. After regression analysis, the compensated material state identification result is generated.

[0050] After completing the state compensation, the control execution evaluation is performed based on the state compensation and control analysis results to establish the control execution evaluation results. Specifically, the compensated data and control parameters are input into the simulation model through the simulation tool to gradually simulate the execution of the current process control. According to these compensated state data and control parameters, multiple simulations are run to check the stability and execution effect of the process control. During the simulation process, the execution of each process step is recorded, especially the control effect when the parameters are close to the critical value. Compare the actual production data and simulation results to evaluate the effectiveness of the current process control strategy. After the simulation is completed, a control execution evaluation result report is generated, which includes the performance during the process control execution process.

[0051] Finally, the material status identification results and control execution evaluation results are used as data analysis results.

[0052] Step S500: Perform material flow traceability management of the tray automation production line based on the data analysis results and the smart labels.

[0053] In the embodiment of the present application, when the material flow traceability management of the tray automated production line is performed based on the data analysis results and the smart label, the unique identification ID in the smart label is first read, and the process execution record in the database is queried based on this ID. Then, the execution status of each process unit is traced one by one according to the data analysis results, and the process step in which the material is abnormal is accurately located, or whether the material meets the process requirements during the entire production process is confirmed.

[0054] Through smart labels, the complete material flow path is obtained, including the execution time, process parameters and material status of each process unit. If a problem occurs, the specific step where the abnormality occurs can be quickly located based on the data analysis results to ensure that the entire process from raw materials to finished products can be accurately tracked and recorded. By combining data analysis results with smart labels, real-time traceability management of the entire production process is achieved, ensuring that any problems can be quickly located and resolved, and achieving transparency in the entire material flow process.

[0055] Furthermore, the method provided in the application embodiment also includes:

[0056] Evaluate the material flow and establish abnormal flow risks, which include abnormal deformation risk and abnormal surface risk. Generate a flow verification partition based on the abnormal flow risk, and set the flow verification partition between process storage partitions. Before each process storage partition is activated, perform consistency verification on the storage results of the preceding process storage partition through the flow verification partition. When the consistency verification passes, the current process storage partition is allowed to be activated.

[0057] In an embodiment of the present application, when evaluating the flow of materials, the outer contour of the material is first measured by a laser displacement sensor, and the measurement result is compared with a preset shape standard. For example, the set deformation standard may be that the maximum allowable change in material thickness in a certain process step is 0.1 mm. If the laser displacement sensor detects that the thickness change exceeds this range, it is marked as "deformation abnormality". The light reflection data on the surface of the material is captured by an optical sensor to identify whether there are scratches, cracks or uneven surfaces. The judgment criteria for surface abnormalities, such as the surface finish must reach a certain light reflection intensity value. If it is lower than this value, it is marked as "surface abnormality". Through this process, the risk of flow abnormality is established, and the risk of flow abnormality includes the risk of deformation abnormality and the risk of surface abnormality.

[0058] Next, a flow verification partition is generated according to the flow abnormality risk, and the flow verification partition is set between the process storage partitions. Specifically, a flow verification partition is set between each process storage partition. The flow verification partition serves as an intermediate verification area to temporarily store the material data of the previous process step. Before the subsequent process step starts, this verification partition must be used to check whether the material status meets the requirements. Before the material enters the next process step, the data of the previous process step stored in the flow verification partition is read. Through the data comparison algorithm, the material deformation data of the previous process step is compared with the sensor data of the current step. For example, if the material thickness of the previous process step is recorded as 1.5 mm, the thickness measurement data in the current process step is read and compared point by point. If the difference is less than the set threshold, such as 0.05 mm, the deformation verification passes; the surface finish data is compared. For example, the surface reflectivity data is recorded as 90% in the previous process step, and the surface status of the current process step is compared. If the reflectivity decreases by more than the set value, such as 5%, the surface consistency verification fails.

[0059] If the consistency verification of deformation data and surface state data is passed and there is no risk of abnormal flow, the current process storage partition is activated.

[0060] Furthermore, the method provided in the application embodiment also includes:

[0061] Determine whether the data analysis results meet the exception determination rules; if the data analysis results meet the exception determination rules, execute the abnormal identification of the material and generate a recycling instruction; based on the recycling instruction, perform the transmission and recycling management of the material according to the abnormal identification.

[0062] In the embodiment of the present application, the data analysis results are compared with the pre-set abnormality determination rules. The abnormality determination rules are thresholds set by technical experts based on process standards and quality requirements. For example, the thickness change of the material cannot exceed a certain maximum allowable value, or the reflectivity of the surface finish must be within a specified range. By comparing each parameter in the data analysis result with these thresholds one by one, if some parameters exceed these preset thresholds, it is judged that the data analysis results meet the abnormality determination rules.

[0063] Once it is determined that the data analysis results meet the abnormality determination rules, the batch of materials will be marked as abnormal. That is, a mark of abnormal status is assigned to the batch of materials, usually adding an abnormal mark to its smart label. After the material is marked as abnormal, a recycling instruction is generated according to the type of abnormality. The recycling instruction includes specific steps on how to deal with the batch of materials, such as whether the materials need to be sent back to a recycling area, or directly reprocessed or discarded. These specific steps are pre-set.

[0064] After the recycling instruction is generated, the material transfer and recycling management is carried out according to the instruction and the abnormal identification of the material.

[0065] Furthermore, the method provided in the application embodiment also includes:

[0066] Accumulate and record the abnormal identifications and establish the cumulative record results; extract the joint abnormalities based on the cumulative record results, and perform process optimization management of the process unit based on the joint abnormalities.

[0067] In an embodiment of the present application, when an abnormality is detected during the material flow process, an abnormality mark is generated for the batch of materials. These abnormality marks are stored in a central database, and the specific process steps, time, abnormality type and impact range of the abnormality are recorded. Whenever a similar abnormality occurs again on the production line, the new abnormal data is added to the existing abnormality record. These abnormality marks are then classified and sorted through data clustering analysis. Specifically, the recorded abnormal data is analyzed using the K-means clustering algorithm, and the abnormal data is clustered according to process steps, abnormality types, etc. In this way, the pattern of frequent abnormalities in specific process units or conditions is identified. For example, in some process steps, the probability of deformation abnormalities occurring is higher when the temperature rises. The abnormal pattern and distribution are established through cluster analysis to generate cumulative record results.

[0068] After the cumulative record results are established, association rule mining techniques, such as the Apriori algorithm, are used to analyze multiple abnormal data to discover the potential correlation between them. By scanning the data in the cumulative record results multiple times, the pattern of multiple anomalies occurring simultaneously is identified, and the frequently occurring abnormal combinations are extracted, which are called joint anomalies. These joint anomalies reflect the phenomenon that multiple anomalies are associated with each other in the same process unit or different process steps. For example, in a certain process unit, deformation anomalies and temperature anomalies often appear at the same time. The Apriori algorithm is used to find this association, extract the relevant frequent item sets, and determine the association rules between anomalies.

[0069] After extracting the joint anomalies, adjust and optimize the parameters for these anomalies. First, determine the key process parameters that cause the joint anomalies. For example, if a strong correlation is found between deformation anomalies and temperature anomalies, optimize the temperature control parameters. Specific operations include adjusting the set value of the temperature controller to reduce the temperature fluctuation range, thereby reducing the occurrence of deformation anomalies. At the same time, adjust other process parameters such as processing time and pressure to ensure the stability of the process flow. After the optimization is completed, verify the effect of these parameter adjustments in actual production. Through real-time monitoring of sensors, continuously collect optimized data, and compare the frequency of anomalies before and after optimization. If the optimization effect is significant and the number of anomalies is reduced, it is considered that the process optimization management of the process unit has been completed.

[0070] In the embodiments of the present application, in summary, the embodiments of the present application have at least the following technical effects:

[0071] Before the raw materials are loaded, the present application configures a smart label that is uniquely mapped to the raw materials, wherein the smart label contains a unique identification ID and raw material data, and the smart label is configured with a preset process storage partition, and the process storage partition is activated through the pre-order partition; when the raw materials are circulated in an automated production line, before any process unit is executed, the label interaction of the smart label is performed to determine whether the process storage partition of the current process is activated; if the process storage partition of the current process is activated, the activated process storage partition is used to record the processing data, and after the current process processing is completed, the smart sensor on the process unit is synchronously activated to perform material status monitoring and establish material status monitoring results; the material status monitoring results are synchronized to the activated process storage partition in real time, and after the current process storage partition is encapsulated, it is sent to the central database to perform data analysis; and the material flow traceability management of the tray automated production line is performed based on the data analysis results and the smart label. The present invention solves the technical problem in the prior art that it is difficult to quickly trace the root cause of the problem when production anomalies occur. By configuring a uniquely mapped smart label for the raw materials before they are loaded, and activating the process storage partition of the label in each process step, accurate recording and real-time monitoring of the material status can be achieved. The data is synchronously monitored by smart sensors and updated to the central database in real time, ensuring that the entire process is traceable, thereby achieving the technical effect of improving the efficiency of production line anomaly management and data analysis.

[0072] Embodiment 2, based on the same inventive concept as the material flow tracing method for the tray automated production line in the previous embodiment, Figure 2 As shown, the present application provides a material flow tracing device for a tray automated production line, and the device and method embodiments in the present application are based on the same inventive concept. The device includes:

[0073] A label configuration module 11, which configures a smart label uniquely mapped to the raw material before the raw material is loaded, wherein the smart label contains a unique identification ID and raw material data, and the smart label is configured with a preset process storage partition, and the process storage partition is activated by a pre-order partition; a label interaction module 12, which performs label interaction of the smart label when the raw material is circulated in an automated production line and before any process unit is executed, to determine whether the process storage partition of the current process is activated; a status monitoring module 13, which is used to determine whether the process storage partition of the current process is activated if the smart label is configured with ... If the process storage partition of the previous process is activated, the activated process storage partition is used to record the processing data, and after the current process is completed, the intelligent sensor on the process unit is synchronously activated to perform material status monitoring and establish material status monitoring results; the data analysis module 14, the data analysis module 14 synchronizes the material status monitoring results to the activated process storage partition in real time, and after encapsulating the current process storage partition, sends it to the central database to perform data analysis; the traceability management module 15, the traceability management module 15 performs material flow traceability management of the tray automation production line according to the data analysis results and the intelligent label.

[0074] Furthermore, the device is also used to achieve the following functions:

[0075] Utilize the central database to parse all process units, perform correlation analysis between process units, and establish process correlation results; after optimizing the central database using the process correlation results, read the processing data record results and material status monitoring results in the storage partition package; perform processing control analysis on the processing data record results to generate control analysis results; perform material status identification on the material status monitoring results to establish material status identification results; call backtracking data according to the process correlation results, perform status compensation for the material status identification results based on the backtracking data, and perform control execution evaluation based on the status compensation and control analysis results to establish control execution evaluation results; complete data analysis with the material status identification results and control execution evaluation results.

[0076] Furthermore, the device is also used to achieve the following functions:

[0077] If the process storage partition of the current process is not activated, a self-check instruction is generated; based on the self-check instruction, a self-check of the storage data of the previous process storage partition is performed, and a self-check result is established; and material return management is performed according to the self-check result.

[0078] Furthermore, the device is also used to achieve the following functions:

[0079] Establish redundant sensors, which are used to perform verification data collection of materials; when materials are returned for management, self-inspection abnormal value judgment is performed based on the self-inspection results; if the self-inspection abnormal value meets the preset threshold, the redundant sensor is activated to re-collect the material data, and the material return management is completed according to the re-collection results.

[0080] Furthermore, the device is also used to achieve the following functions:

[0081] Evaluate the material flow and establish abnormal flow risks, which include abnormal deformation risk and abnormal surface risk. Generate a flow verification partition based on the abnormal flow risk, and set the flow verification partition between process storage partitions. Before each process storage partition is activated, perform consistency verification on the storage results of the preceding process storage partition through the flow verification partition. When the consistency verification passes, the current process storage partition is allowed to be activated.

[0082] Furthermore, the device is also used to achieve the following functions:

[0083] Determine whether the data analysis results meet the exception determination rules; if the data analysis results meet the exception determination rules, execute the abnormal identification of the material and generate a recycling instruction; based on the recycling instruction, perform the transmission and recycling management of the material according to the abnormal identification.

[0084] Furthermore, the device is also used to achieve the following functions:

[0085] Accumulate and record the abnormal identifications and establish the cumulative record results; extract the joint abnormalities based on the cumulative record results, and perform process optimization management of the process unit based on the joint abnormalities.

[0086] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. The processes depicted in the accompanying drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0087] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0088] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A material flow tracing method for a tray automated production line, characterized in that: The method comprises: Before the raw materials are loaded, a smart label uniquely mapped to the raw materials is configured, wherein the smart label contains a unique identification ID and raw material data, and the smart label is configured with a preset process storage partition, and the process storage partition is activated through the pre-order partition; when the raw materials enter the automated production line, it is determined whether the previous process step is completed and its data is correctly stored; if the verification is passed, the storage partition of the current process is activated, and the relevant data in the processing process continues to be recorded; when the raw materials are circulated in the automated production line, before any process unit is executed, the label interaction of the smart label is executed to determine whether the process storage partition of the current process is activated; If the process storage partition of the current process is not activated, a self-test instruction is generated; Perform a self-check of the storage data of the previous process storage partition based on the self-check instruction, and establish a self-check result; Carry out material return management based on self-inspection results; If the process storage partition of the current process is activated, the activated process storage partition is used to record the processing data, and after the current process is completed, the intelligent sensor on the process unit is synchronously activated to perform material status monitoring and establish the material status monitoring results; Synchronize the material status monitoring results to the activated process storage partition in real time, and package the current process storage partition and send it to the central database for data analysis; The packaging of the current process storage partitions is sent to the central database to perform data analysis, and further includes: Utilize the central database to analyze all process units, perform correlation analysis between process units, and establish process correlation results; After optimizing the central database using the process correlation results, read the processing data record results and material status monitoring results in the storage partition package; Performing processing control analysis on the processing data record results to generate control analysis results; Perform material status identification on the material status monitoring results and establish material status identification results; Call the backtracking data according to the process association results, perform status compensation of the material status identification results based on the backtracking data, and perform control execution evaluation based on the status compensation and control analysis results to establish the control execution evaluation results; Complete data analysis with material status identification results and control execution evaluation results; Conduct material flow traceability management of tray automation production lines based on data analysis results and smart labels; Evaluate the material flow and establish abnormal flow risks, including abnormal deformation risk and abnormal surface risk; Generate a flow verification partition based on the risk of flow abnormality, and set the flow verification partition between process storage partitions. Before each process storage partition is activated, the storage result consistency of the previous process storage partition is verified through the flow verification partition. When the consistency verification passes, the current process storage partition is allowed to be activated.

2. The material flow tracing method for the tray automated production line according to claim 1, characterized in that: The material return management according to the self-inspection results also includes: Establishing redundant sensors, the redundant sensors are used to perform verification data collection of materials; When the material is returned for management, self-inspection abnormal value determination is performed based on the self-inspection results; If the self-test abnormal value meets the preset threshold, the redundant sensor is activated to re-collect the material data, and the material return management is completed according to the re-collection results.

3. The material flow tracing method for a tray automated production line according to claim 1, characterized in that: The method further comprises: Determine whether the data analysis results meet the abnormality determination rules; If the data analysis results meet the abnormality determination rules, the abnormal identification of the material is executed and a recycling instruction is generated; Based on the recycling instructions, the material transfer and recycling management is carried out according to the abnormal identification.

4. The material flow tracing method for a tray automated production line according to claim 3, characterized in that: The method further comprises: Accumulate and record the abnormal identifications and establish the cumulative record results; Extract joint anomalies based on the accumulated record results, and perform process optimization management of the process unit based on the joint anomalies.

5. A material flow tracing device for a tray automated production line, characterized in that: The device comprises: A label configuration module, which configures a smart label uniquely mapped to the raw material before the raw material is loaded, wherein the smart label contains a unique identification ID and raw material data, and the smart label is configured with a preset process storage partition, and the process storage partition is activated by the pre-order partition. When the raw material enters the automated production line, it is determined whether the previous process step is completed and its data is correctly stored; if the verification is passed, the storage partition of the current process is activated, and the relevant data in the processing process continues to be recorded; A label interaction module, which performs label interaction of smart labels when raw materials are circulated in an automated production line and before any process unit is executed, and determines whether the process storage partition of the current process is activated; A status monitoring module, which is used to generate a self-check instruction if the process storage partition of the current process is not activated; perform a self-check of the storage data of the previous process storage partition based on the self-check instruction and establish a self-check result; perform material return management according to the self-check result; if the process storage partition of the current process is activated, use the activated process storage partition to record processing data, and after the current process processing is completed, synchronously activate the intelligent sensor on the process unit to perform material status monitoring and establish a material status monitoring result; a data analysis module, which synchronizes the material status monitoring result to the activated process storage partition in real time, and sends the current process storage partition to the central database after packaging, and executes data analysis. According to the analysis; use the central database to parse all process units, perform correlation analysis between process units, and establish process correlation results; after optimizing the central database with the process correlation results, read the processing data record results and material status monitoring results in the storage partition package; perform processing control analysis on the processing data record results to generate control analysis results; perform material status identification on the material status monitoring results to establish material status identification results; call the backtracking data according to the process correlation results, perform status compensation of the material status identification results based on the backtracking data, and perform control execution evaluation based on the status compensation and control analysis results to establish control execution evaluation results; complete data analysis with the material status identification results and control execution evaluation results; A traceability management module is provided, wherein the traceability management module performs material flow traceability management of the tray automation production line according to the data analysis results and the smart labels; the material flow is evaluated and the flow abnormality risk is established, wherein the flow abnormality risk includes the deformation abnormality risk and the surface abnormality risk; a flow verification partition is generated according to the flow abnormality risk, and the flow verification partition is set between the process storage partitions. Before each process storage partition is activated, the storage result consistency of the preceding process storage partition is verified through the flow verification partition. When the consistency verification passes, the current process storage partition is allowed to be activated.

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