Metal screen product full life cycle quality tracing method and system

Abstract

The invention belongs to the technical field of metal screen production, and particularly relates to a metal screen product full life cycle quality tracing method, which comprises the following steps: when monitoring that a detection node in a metal screen production process has an abnormal state, executing the following steps: identifying the detection node with the abnormal state as a risk point, calculating a risk early warning value of the risk point, and recording the risk early warning value of the risk point; and determining a risk level according to the risk early warning value. In the production process of the metal screen, the production nodes are classified into the early-stage operation stage and the later-stage operation stage according to the production stages where the production nodes are located, then the detection nodes are arranged in the production nodes, and the detection nodes are identified as risk points when the states of the detection nodes are abnormal; the risk points are calibrated as the first-level risk points or the second-level risk points according to the production stages of the risk points, early recognition and hierarchical management of risks in the production process are achieved, the risk points in different stages are subjected to differential processing, and the situation that all state anomalies are subjected to undifferentiated processing is avoided.

Description

Technical Field

[0001] This invention belongs to the field of metal screen production technology, specifically relating to a method and system for tracing the quality of metal screen products throughout their entire lifecycle. Background Technology

[0002] As a key screening and filtration material, the quality management and effective quality traceability of metal screen products are of great significance for ensuring production safety, reducing operating costs, and meeting industry standards.

[0003] In existing technologies, a reactive response mechanism is typically adopted, recording and handling risks or quality problems only after they have occurred. Enterprises cannot detect and correct production anomalies in a timely manner, resulting in missed opportunities for early intervention. They lack the ability to predict and warn of potential production anomalies, making it difficult for enterprises to prevent problems before they occur in the face of complex and ever-changing production environments. Due to the lack of a forward-looking anomaly detection mechanism, when batch quality problems occur, a lot of manpower and resources are needed for investigation and rework, extending the product production cycle and leading to the production of defective products. This passive quality management model not only reduces production efficiency but also restricts the further development of enterprises in the direction of automated manufacturing.

[0004] In view of this, the present invention proposes a method and system for quality traceability throughout the entire life cycle of metal screen products. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for quality traceability throughout the entire life cycle of metal screen products. This invention can predict abnormal production links in advance, ensure normal production and product quality, determine the corresponding early warning level, and execute different level of handling plans based on this.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is: a method for quality traceability throughout the entire life cycle of metal screen products, comprising: when an abnormal state is detected at a detection node in the metal screen production process, performing the following steps: identifying the detection node with the abnormal state as a risk point, calculating the risk warning value of the risk point, and determining the risk level based on the risk warning value;

[0007] The calculation of risk warning values ​​for risk points includes: collecting data for the corresponding risk points as data to be evaluated, screening the data to be evaluated to identify data items that are directly related to the abnormal status as key data, and calculating risk warning values ​​based on the key data;

[0008] Calculating risk warning values ​​based on key data includes: arranging key data related to the risk point in chronological order to obtain a processing sequence; combining the rated range of the key data to calculate the percentage of excess data within a unit time period as a key indicator; and determining the risk warning value of the risk point based on the key indicator.

[0009] Preferably, before identifying the detection node with an abnormal state as a risk point, the method further includes: acquiring the metal screen production process and classifying the production nodes in the metal screen production process according to their production stage. The production stage includes the early operation stage and the late operation stage. The risk point includes primary risk point and secondary risk point. The primary risk point is the risk point located in the early operation stage, and the secondary risk point is the risk point located in the late operation stage.

[0010] Preferably, screening the data to be evaluated includes: identifying data items other than key data as auxiliary data, where key data reflects the main reasons for the abnormal state of risk points, and auxiliary data reflects the secondary reasons for the abnormal state of risk points.

[0011] Preferably, determining the risk level based on the risk warning value and performing risk response processing corresponding to the risk level includes: comparing the risk warning value with the warning threshold and the response threshold; if the risk warning value exceeds the warning threshold but does not exceed the response threshold, the risk level is determined to be a Level 1 warning, and data collection and determination continue.

[0012] If the risk warning value exceeds the response threshold, the risk level is determined to be a Level 2 warning, and the production node corresponding to the risk point is interrupted; the warning threshold and response threshold are matched and corrected according to the historical changes of the risk point; the matched and corrected warning threshold and response threshold are used in the step of determining the risk level.

[0013] Preferably, the method further includes: acquiring stored metal screen product testing data and comparing it with normal output data corresponding to risk points associated with the metal screen product to determine the similarity; if the similarity is lower than the similarity threshold, the metal screen product is labeled as an abnormal product.

[0014] A quality traceability system for the entire lifecycle of metal mesh products includes:

[0015] The risk point identification module is used to monitor the status of detection nodes in the metal screen production process, so as to identify the corresponding risk points when abnormal status is detected.

[0016] The risk level determination module is used to calculate the risk warning value based on the identification results of the risk point identification module and the key data associated with the risk point, and compare the risk warning value with the warning threshold and the response threshold to determine the risk level.

[0017] The risk response processing module is used to perform risk response processing corresponding to the risk level determined by the risk level determination module.

[0018] The threshold correction module is used to match and correct the warning threshold and the response threshold based on the historical changes of the risk points;

[0019] The product quality traceability module is used to establish the link between production process risks and final product quality.

[0020] Preferably, the risk point identification module includes: acquiring the metal screen production process, and classifying the production nodes in the metal screen production process according to their production stages, including the early operation stage and the later operation stage, and the risk points including primary risk points and secondary risk points, with primary risk points being risk points located in the early operation stage and secondary risk points being risk points located in the later operation stage.

[0021] Preferably, the risk level determination module is also used to: calculate the risk warning value of the risk point, collect the data of the corresponding risk point as the data to be evaluated, screen the data to be evaluated to determine the data items that are directly related to the abnormal state as key data, and calculate the risk warning value based on the key data;

[0022] Furthermore, risk warning values ​​are calculated based on key data. Key data related to risk points are arranged in chronological order to obtain a processing sequence. Combined with the rated range of key data, the proportion of excess data within a unit time period is calculated as a key indicator, and the risk warning value of the risk point is determined based on the key indicator.

[0023] Preferably, the risk response processing module is further configured to: determine the risk level based on the risk warning value, and execute the risk response processing corresponding to the risk level; compare the risk warning value with the warning threshold and the response threshold; if the risk warning value exceeds the warning threshold but does not exceed the response threshold, the risk level is determined to be a Level 1 warning, and data collection and determination continue; if the risk warning value exceeds the response threshold, the risk level is determined to be a Level 2 warning, and the production node corresponding to the risk point is interrupted.

[0024] Preferably, the threshold correction module is further configured to: match and correct the warning threshold and the response threshold based on the historical changes of the risk point; and use the matched and corrected warning threshold and response threshold in the step of determining the risk level.

[0025] Beneficial effects

[0026] 1. This invention classifies production nodes in the metal screen production process into early operation stages and late operation stages according to their production stage. Then, detection nodes are set within the production nodes. When an abnormal state is detected at a detection node, it is identified as a risk point. Based on its production stage, the risk point is marked as a first-level risk point or a second-level risk point. This enables early identification and hierarchical management of risks in the production process, allowing risk points at different stages to be treated differently, avoiding indiscriminate treatment of all abnormal states.

[0027] 2. This invention screens the collected data to be evaluated, identifies data items directly related to abnormal status as key data, and other data items as auxiliary data. Based on the key data, key indicators are calculated to determine risk warning values, identify the main causes of abnormal product output, and then accurately reflect the abnormal status trend of risk points through the analysis and quantification of key data. This provides a quantitative basis for subsequent risk warning and risk response, improving the accuracy and reliability of risk assessment.

[0028] 3. This invention determines the risk level by comparing the risk warning value of a risk point with the warning threshold and response threshold, and then performs the corresponding risk response processing according to the risk level. When the risk is determined to be a level one warning, data collection and judgment continue, while when the risk is determined to be a level two warning, the production node corresponding to the risk point is interrupted, thereby realizing dynamic monitoring and graded response to production risks and taking appropriate measures when the risk reaches different levels.

[0029] 4. This invention obtains the test data of metal screen products and compares it with the normal output data corresponding to relevant risk points to determine the similarity, and then determines whether the product is a normal product or an abnormal product. This method can perform quality traceability and anomaly identification of products, promptly discover and mark abnormal products, prevent abnormal products from entering the market, and realize a closed loop of quality management throughout the entire life cycle of metal screen products. Attached Figure Description

[0030] Figure 1 This is a flowchart of the method for outputting risk level determination and response strategy according to the present invention;

[0031] Figure 2 This is a flowchart of the method for comparing and identifying product testing data according to the present invention;

[0032] Figure 3 This is a system module diagram of the present invention. Detailed Implementation

[0033] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. The following are merely preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention, which is defined by the appended claims and their equivalents.

[0034] Example 1

[0035] Please see Figure 1-2 As shown in the figure, this embodiment discloses a method for quality traceability throughout the entire life cycle of metal screen products. This method can be used to proactively identify, dynamically respond to, and trace various risks throughout the entire production cycle of metal screen products, thereby reducing the defect rate and improving product traceability and customer satisfaction. The specific steps of this method are as follows:

[0036] Production process acquisition and stage classification: The metal screen production process is acquired and broken down into multiple independent production nodes. Each production node is the smallest manageable unit that constitutes the metal screen production process, such as cutting, punching, stretching, etc. Then, based on the functional attributes of each production node and its logical order in the entire production chain, the nodes are divided into early operation stage and late operation stage.

[0037] The early operation phase includes basic construction steps before product molding, in order to lay the foundation for the physical structure and chemical properties of the final product. Specifically, this involves the precise cutting of raw materials, followed by a high-precision stamping or stretching physical molding process, or cleaning of the surface and chemical or physical pretreatment of the materials.

[0038] In the later stages of operation, in order to control the final performance and factory condition of the product, including molding, surface treatment, quality inspection, etc., the purpose of this stage division is to reflect the different focuses on the impact on product quality in the production process. By implementing differentiated risk management and data traceability strategies, the efficiency and targeting of traceability can be improved.

[0039] Furthermore, risk point identification and data collection involve setting up multiple detection nodes in each defined production node to monitor the operating status of production equipment, key indicators of raw materials, and environmental parameters in real time. For example, in the raw material preparation stage, the detection nodes will continuously monitor the batch information, chemical composition, and physical strength of the raw materials; in the molding stage, the detection nodes will monitor the operating temperature, pressure, vibration frequency, and processing speed of the equipment in real time.

[0040] During the surface treatment stage, the detection nodes monitor key process parameters such as the concentration of the electroplating solution, current density, and processing time. If any detection node shows an abnormal signal, such as a sensor reading exceeding the preset safety range (i.e., exceeding the upper or lower limit of the parameter's allowable fluctuation under normal production conditions), automatic alarm of the equipment, or manual input of an abnormal report by the operator through the human-machine interface, then the node is determined to have a potential risk and is marked as a risk point.

[0041] Risk points are classified according to their production stage: Risk points that occur in the early stage of operation are usually classified as first-level risk points. For example, when the raw material composition is unqualified or the initial molding size deviation is too large, these risks are more troublesome and may cause a chain reaction in the subsequent production process. A longer correction window is required to allow preventive measures to be taken.

[0042] Risks that emerge in the later stages of operation are classified as secondary risk points. These risk points directly affect the final quality of the product and are costly to correct or, in some cases, impossible to correct. Therefore, they require immediate response and corrective measures. For each identified risk point, a data acquisition mechanism is immediately activated to collect various operational data, sensor output values, and processing parameters when the risk point exists. These data are then used as data to be evaluated.

[0043] Furthermore, the screening and classification of key and auxiliary data, in-depth analysis of the data to be evaluated, and setting of a target deviation range are all part of the process. This range precisely defines the allowable fluctuation range of various parameters under normal production conditions, such as the upper and lower limits of equipment operating temperature and the percentage range of raw material chemical composition. All data to be evaluated are input as evaluation items and compared one by one with the target deviation range.

[0044] If the data to be evaluated contains data items that are higher than the target deviation range, and these data items are preset as key parameters affecting product quality, in the process of metal screen weaving, if the tension parameter of the weaving machine exceeds the target deviation range, based on past experience, after expert experience or historical data analysis, this tension parameter is determined to be the core factor affecting the uniformity of the mesh, then this data item is marked as key data, representing the main cause of abnormal product quality.

[0045] Among the remaining data to be evaluated, apart from the data items that have been marked as key data, such as the slight fluctuations in workshop environmental humidity within the allowable range, although they may also affect the production process, their impact is far less than that of key parameters. Therefore, they are marked as auxiliary data by the system, representing secondary influencing factors. The classification method aims to focus the analysis on the factors that have the greatest impact on product quality, thereby improving the efficiency and accuracy of risk identification and handling.

[0046] Meanwhile, when the monitored temperature fluctuations are still within the equipment's tolerance range, the vibration parameter records will be classified as key data, while the temperature parameter records will be classified as auxiliary data.

[0047] Furthermore, the risk warning value is calculated. For each identified key data, the time period and frequency of its value change are statistically analyzed. Combined with the identified abnormal nodes, the period between adjacent initial and final time points is defined as a unit time period, which is the period when the key data is continuously abnormal.

[0048] Specifically, the rated range corresponding to each key data point is obtained, that is, the allowable fluctuation range of the key data under normal working conditions. The deviation of the key data in different time periods is calculated, and the difference between the actual measured value of the key data and the center value of the rated range is calculated. The deviation is checked to see if it is greater than the rated range, that is, whether it exceeds the normal fluctuation range. If the deviation is greater than the rated range, the key data is judged as an abnormal node, and the time point when the first abnormality occurs is recorded as the initial time point. If the deviation is not greater than the rated range, the key data is continuously monitored.

[0049] Then, it is cumulatively judged whether it is continuously within the normal range over the duration. If the cumulative number exceeds the preset termination threshold, such as 10 consecutive deviations within the rated range, the time point when the abnormal node returns to normal is recorded as the end time point. The time period between adjacent initial time points and end time points is defined as a unit time period. This unit time period represents the period during which the key data is continuously abnormal. In each unit time period, the proportion of the corresponding parameter that exceeds the rated range is calculated and defined as the excess data proportion.

[0050] For example, if key data exceeds the rated range for 70% of the time within a unit of time, then the excess data percentage is 70%. This excess data percentage is used to describe the abnormal trend of the risk point. The higher the excess data percentage, the more obvious the abnormal trend. Based on the changing trend of the excess data percentage, a set of preset risk assessment rules is used to calculate the risk warning value of the current risk point. That is, the absolute value of the excess data percentage, the rate of change, the duration, and the correlation in historical data are comprehensively considered to calculate the risk warning value of the current risk point. The higher the value, the greater the degree of risk.

[0051] Furthermore, the risk level determination and response strategy compares the current risk warning value of all risk points with the set warning threshold and response threshold. The warning threshold is used to determine whether a risk point needs to issue a warning signal, and the response threshold is used to determine whether an emergency response measure needs to be taken. The operation is performed according to the following judgment logic, and the warning threshold and response threshold are matched and corrected according to the historical changes of the risk points.

[0052] Specifically, historical data of the corresponding risk point is analyzed, such as the average fluctuation range of the risk warning value, the frequency of anomalies, and the duration of anomalies over a period of time. Combined with the instantaneous anomaly intensity, deviation velocity, and stability coefficient, a correction factor is calculated. This correction factor reflects the inherent characteristics and change patterns of the risk point under different production conditions. The original warning threshold and response threshold parameters are adjusted using this correction factor to obtain new comparison benchmark values, namely the corrected warning threshold and the corrected response threshold.

[0053] The dynamic correction mechanism enables the early warning system to better adapt to the complexity and variability of the actual production environment, effectively avoiding false alarms or missed alarms. The current risk warning value of the risk point is compared with the corrected warning threshold and the corrected response threshold in sequence: if the current risk warning value of the risk point exceeds the corrected warning threshold but does not exceed the corrected response threshold, a first-level warning signal is output.

[0054] Level 1 warning signals can alert operators through the system display interface, warning lights, or other means, such as displaying a yellow warning on the control panel and automatically generating suggestions for prioritizing inspection plans or adjusting relevant process parameters in order to intervene before the risk escalates. If the current risk warning value exceeds the corrected response threshold, a Level 2 warning signal will be output and the production node to which the current risk point belongs will be immediately interrupted.

[0055] Triggering system alarms or switching to backup equipment: For example, if the vibration risk warning value of critical equipment exceeds the response threshold, the operation of the equipment will be stopped immediately and maintenance personnel will be notified. At the same time, it may automatically switch to the backup production line to prevent continued production and avoid the generation of abnormal products. If the current risk warning value does not reach the warning threshold, it means that the risk is within a controllable range and there is no need to interrupt operation.

[0056] Furthermore, product inspection data comparison and anomaly identification are carried out. During the production process, metal screen product inspection data are collected regularly, including dimensional accuracy, aperture distribution, mesh surface flatness, and surface coating thickness. This data can be obtained through automated equipment such as high-precision measuring instruments and machine vision systems. These real-time collected metal screen product inspection data are compared item by item with the normal output data stored in the system, and their similarity is calculated. The normal output data is a benchmark dataset based on historical qualified products or design standards, which represents the ideal state of the product.

[0057] The comparison process employs parameter difference analysis, which calculates the difference between multiple parameters of the metal screen product test data and the normal output data item by item, and compares them with the rated deviation range of the parameters to determine whether the product is abnormal. For example, for dimensional parameters, the system calculates the difference between the actual size and the standard size and compares it with the allowable dimensional tolerance. If the overall deviation is higher than the rated deviation range, it is judged as similar, that is, there is a significant difference between the metal screen product test data and the normal output data, indicating that the product may be abnormal. If the overall deviation is lower than the set proportion, it is judged as dissimilar, that is, the metal screen product test data and the normal output data are highly consistent, and the product quality is good. A similarity threshold is set to determine the critical value of the similarity between the product test data and the normal data.

[0058] If the similarity is below the similarity threshold, the product is considered a normal product; if the similarity is higher than or equal to the similarity threshold, the product is marked as an abnormal product. For example, if the similarity calculation result shows that the difference between the product and the normal benchmark exceeds 5%, and that 5% is the similarity threshold, then the product will be marked as an abnormal product.

[0059] Example 2

[0060] Please see Figure 3 As shown, this embodiment provides a full lifecycle quality traceability system for metal screen products, enabling precise quality traceability and closed-loop management throughout the product's entire lifecycle. In practical implementation, it can be deployed on a server, industrial control computer, or cloud platform, and interact with and control various sensors, actuators, and manufacturing execution systems (MES) in the metal screen production process via industrial Ethernet, fieldbus, or other methods.

[0061] Specifically, the system includes the following modules:

[0062] The risk point identification module continuously monitors the status of the metal screen production process and obtains the predefined metal screen production process, which includes all production nodes from raw material preparation to finished product output. All production nodes are divided into early operation stage and late operation stage according to their stage in the entire process.

[0063] During system operation, the status data of each detection node in the process is monitored in real time, such as temperature, pressure, and mesh uniformity. When the data of any detection node deviates from its normal operating range, i.e., an abnormal status occurs, the detection node is immediately identified as a risk point.

[0064] Generally, risk points are classified according to the production stage in which they occur: when a risk point is in the early stage of operation, it is identified as a level 1 risk point; when it is in the later stage of operation, it is identified as a level 2 risk point. The system then sends these identified risk points and their level information to the risk level determination module for further analysis.

[0065] The risk level assessment module is activated after receiving risk point information from the risk point identification module. Its core task is to quantify the severity of the risk. It collects a set of data directly related to the risk point as the data to be assessed, and then screens and analyzes this data to determine the data items with the most direct causal relationship to the monitored anomalies. These data items are then identified as key data, reflecting the main causes of the anomalies.

[0066] Other data items in the data to be evaluated, besides the key data, are identified as auxiliary data. Auxiliary data can be used to reflect secondary causes or related influencing factors of abnormal status, and risk warning values ​​for risk points are calculated based on the key data. In a specific calculation process, the key data related to the risk point are arranged in the order of collection time to form a processing sequence. Then, combined with the preset rated range for the key data, the number of data points in the processing sequence that exceed the rated range is counted within a set unit time period, and the proportion of the data points exceeding the rated range is calculated. This proportion is the excess data ratio, which is used as a key indicator to finally determine the risk warning value for the risk point.

[0067] After calculating the risk warning value, it is compared with the warning threshold and the response threshold to determine the risk level. If the risk warning value exceeds the warning threshold but does not exceed the response threshold, the current risk level is determined to be a Level 1 warning. If the risk warning value further exceeds the response threshold, the risk level is determined to be a Level 2 warning, and the determined risk level result is output to the risk response processing module.

[0068] The risk response processing module determines the risk level output by the risk level and then executes the corresponding risk response processing. When the received risk level is a Level 1 warning, it indicates that the current abnormal state is still within a controllable range. If there is a worsening trend, it is only necessary to continue to collect data and determine risks by the risk point identification module and the risk level determination module to closely track the status changes of the risk point, without immediately stopping production. When the received risk level is a Level 2 warning, it indicates that the abnormal state is more serious and may have a substantial impact on product quality. It is necessary to immediately generate control instructions to interrupt the production node directly corresponding to the risk point, such as stopping the operation of related equipment or suspending the production of the current batch, and sending an alarm to the management personnel until the abnormal state is handled.

[0069] The core objective of the threshold correction module is to achieve adaptive optimization of the system's early warning model. Based on historical data, it analyzes the historical changes of identified risk points, such as the fluctuation trend of risk warning values, the frequency of occurrence of warnings at different levels, and the transformation relationship. It then performs periodic or dynamic calibration of the warning threshold and response threshold based on practical experience. For example, if a risk point frequently triggers a Level 1 warning but rarely escalates to a Level 2 warning, its warning threshold can be appropriately increased to reduce the false alarm rate. Conversely, if a risk point deteriorates rapidly once an anomaly occurs, its response threshold may be lowered to achieve faster intervention.

[0070] By updating the corrected warning and response thresholds to the risk level determination module and using them in subsequent risk level determination processes, the accuracy and efficiency of the system's warning mechanism can be improved.

[0071] The product quality traceability module is responsible for establishing the correlation between production process risks and final product quality. When a batch of metal screen products is completed, it acquires the stored metal screen product testing data for that batch, then traces all risk points associated with that batch during the production process, and obtains the normal output data of these risk points under normal production conditions as a benchmark. The metal screen product testing data is then compared and analyzed with the corresponding normal output data to calculate the similarity between the two. When the calculated similarity is lower than the preset similarity threshold, it indicates that the final quality characteristics of the product are significantly different from its characteristics under ideal production conditions. The metal screen product is then marked as an abnormal product, and its correlation with risk events that occurred during the production process is recorded, providing data support for quality problem investigation and process improvement.

[0072] Through the collaborative work of the above modules, the system in this embodiment constructs a closed-loop management system from production process risk warning to final product quality traceability. It can not only proactively identify and respond to quality fluctuations in the production process, but also continuously optimize its judgment ability through adaptive threshold adjustment, and correlate process control data with product quality results to improve the yield rate of precision metal screen manufacturing.

Claims

1. A method for tracing the quality of metal mesh products throughout their entire lifecycle, characterized in that, include: When an abnormal state is detected at a detection node in the metal screen production process, the following steps are taken: identify the detection node with the abnormal state as a risk point, calculate the risk warning value of the risk point, and determine the risk level based on the risk warning value; The calculation of risk warning values ​​for risk points includes: collecting data of the corresponding risk points as data to be evaluated, screening the data to be evaluated to identify data items directly related to the abnormal status as key data, and calculating risk warning values ​​based on the key data. The calculation of risk warning values ​​based on key data includes: arranging key data related to risk points in chronological order to obtain a processing sequence; combining the rated range of key data to calculate the proportion of excess data within a unit time period as a key indicator; and determining the risk warning value of the risk point based on the key indicator.

2. The method for full life-cycle quality traceability of metal screen products according to claim 1, characterized in that, Before identifying the detection nodes with abnormal states as risk points, the process also includes: acquiring the metal screen production process and classifying the production nodes in the metal screen production process according to their production stages. The production stages include the early operation stage and the late operation stage. Risk points include primary risk points and secondary risk points. Primary risk points are those located in the early operation stage, and secondary risk points are those located in the late operation stage.

3. The method for full life-cycle quality traceability of metal screen products according to claim 1, characterized in that, Screening the data to be evaluated includes: identifying data items other than key data as auxiliary data. Key data reflects the main reasons for the abnormal status of risk points, while auxiliary data reflects the secondary reasons for the abnormal status of risk points.

4. The method for full life-cycle quality traceability of metal screen products according to claim 1, characterized in that, The risk level is determined based on the risk warning value, and the corresponding risk response is executed, including: comparing the risk warning value with the warning threshold and the response threshold; if the risk warning value exceeds the warning threshold but does not exceed the response threshold, the risk level is determined to be a Level 1 warning, and data collection and determination continue. If the risk warning value exceeds the response threshold, the risk level is determined to be a Level 2 warning, and the production node corresponding to the risk point is interrupted; the warning threshold and response threshold are matched and corrected according to the historical changes of the risk point; the matched and corrected warning threshold and response threshold are used in the step of determining the risk level.

5. The method for full life-cycle quality traceability of metal screen products according to claim 1, characterized in that, The method also includes: acquiring stored metal screen product testing data and comparing it with normal output data corresponding to risk points associated with the metal screen product to determine the similarity; if the similarity is lower than the similarity threshold, the metal screen product is marked as an abnormal product.

6. A quality traceability system for the entire lifecycle of metal screen products, characterized in that, include: The risk point identification module is used to monitor the status of detection nodes in the metal screen production process, so as to identify the corresponding risk points when abnormal status is detected. The risk level determination module is used to calculate the risk warning value based on the identification results of the risk point identification module and the key data associated with the risk point, and compare the risk warning value with the warning threshold and the response threshold to determine the risk level. The risk response processing module is used to perform risk response processing corresponding to the risk level determined by the risk level determination module. The threshold correction module is used to match and correct the warning threshold and the response threshold based on the historical changes of the risk points; The product quality traceability module is used to establish the link between production process risks and final product quality.

7. A metal screen product lifecycle quality traceability system according to claim 6, characterized in that, The risk point identification module includes: acquiring the metal screen production process and classifying the production nodes in the metal screen production process according to their production stages. The production stages include the early operation stage and the late operation stage. The risk points include primary risk points and secondary risk points. Primary risk points are those located in the early operation stage, and secondary risk points are those located in the late operation stage.

8. A metal screen product lifecycle quality traceability system according to claim 6, characterized in that, The risk level determination module is also used to: calculate the risk warning value of the risk point, collect the data of the corresponding risk point as the data to be evaluated, screen the data to be evaluated to determine the data items that are directly related to the abnormal status as key data, and calculate the risk warning value based on the key data; Furthermore, risk warning values ​​are calculated based on key data. Key data related to risk points are arranged in chronological order to obtain a processing sequence. Combined with the rated range of key data, the proportion of excess data within a unit time period is calculated as a key indicator, and the risk warning value of the risk point is determined based on the key indicator.

9. A metal screen product lifecycle quality traceability system according to claim 6, characterized in that, The risk response processing module is also used to: determine the risk level based on the risk warning value, and execute the risk response processing corresponding to the risk level. It compares the risk warning value with the warning threshold and the response threshold. If the risk warning value exceeds the warning threshold but does not exceed the response threshold, the risk level is determined to be a level one warning, and data collection and judgment continue. If the risk warning value exceeds the response threshold, the risk level is determined to be a Level 2 warning, and the production node corresponding to the risk point is interrupted.

10. A metal screen product lifecycle quality traceability system according to claim 9, characterized in that, The threshold correction module is also used to: match and correct the warning threshold and response threshold according to the historical changes of the risk point; and use the matched and corrected warning threshold and response threshold in the step of determining the risk level.

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