A control method and system for a stacking device of an automated glue injection production line

Abstract

The embodiment of the application provides a stacking device control method and system for an automatic glue injection production line, and relates to the technical field of automatic production line control. The method comprises the following steps: during production operation, real-time performance parameters of a stacking device clamp when performing a grabbing action are acquired, and real-time performance parameters are obtained, wherein the performance parameters comprise vacuum establishment time and / or negative pressure leakage speed; reference performance parameters when performing the grabbing action are acquired; according to the reference performance parameters and the real-time performance parameters, the performance degradation type of the clamp is judged; according to the performance degradation type of the clamp, the risk level of the performance of the stacking device clamp is determined; according to the risk level, early warning information is sent, and guidance information for maintenance operation is provided. The application realizes predictive maintenance, significantly improves the operation efficiency and reliability of the automatic production line, and reduces production cost and maintenance risk.

Description

Technical Field

[0001] This application relates to the field of automated production line control technology, and more specifically, to a stacking device control method and system for an automated glue dispensing production line. Background Technology

[0002] In automated production lines, especially in processes involving precision adhesive coating, the efficiency and reliability of material handling are crucial. Stacking devices, typically composed of industrial robots and specialized grippers, are responsible for the orderly transfer and stacking of processed workpieces. However, in actual production, slight adhesive overflow may occur during the application process, known as "overflow." When the grippers of the stacking device, particularly vacuum suction cups, contact and pick up these workpieces with uncured overflow, some adhesive transfers to the contact surface of the suction cups.

[0003] As the stacking device performs hundreds or thousands of repeated gripping actions, residual adhesive forms a film on the suction cup surface. This film fundamentally alters the physical properties of the suction cup surface. Vacuum suction cups rely on a sealed space between the suction cup edge and the workpiece surface, using vacuum to create negative pressure and attract the workpiece. The accumulated adhesive film disrupts the smoothness and flexibility of the suction cup lip, causing tiny leaks when it adheres to the workpiece surface. This makes it take longer for the vacuum system to build up sufficient negative pressure, or in some cases, it may not be able to reach the preset stable negative pressure value at all.

[0004] Eventually, when adhesive contamination accumulates to a certain level, the suction force of the suction cup will drop to a critical point. At this point, during high-speed movement or turning of the robotic arm, due to inertia, the poorly held workpiece may experience a slight positional shift, or even be directly thrown off. Once the workpiece falls, it triggers more serious equipment failures, causing the entire production line to shut down. Traditional control systems respond very passively to such problems; they can only detect the final failure result, such as "grab failure" or "no workpiece at the placement position," by checking the presence or absence of sensors installed at the end of the robotic arm or at the tray station. However, they cannot anticipate or prevent this progressive failure process caused by clamp contamination. Summary of the Invention

[0005] This application discloses a stacking device control method and system for an automated glue dispensing production line, which aims to solve the problem that the performance of the stacking device fixtures on the automated production line gradually deteriorates due to glue accumulation, which in turn leads to workpiece falling, scrapping, or even production line shutdown, as well as the shortcomings of traditional control systems that cannot predict and prevent such failure processes.

[0006] In a first aspect, this application discloses a method for controlling a stacking device in an automated dispensing production line, the method comprising: During production operation, the performance parameters of the stacking device clamps performing gripping actions are acquired in real time to obtain real-time performance parameters, including vacuum build-up time and / or negative pressure leakage rate. Obtain baseline performance parameters when performing the grabbing action; Determine the type of performance degradation of the fixture based on the baseline performance parameters and real-time performance parameters; Determine the risk level of the stacking device's clamp performance based on the type of clamp performance degradation. Based on the risk level, issue early warning information and provide guidance on maintenance operations.

[0007] Furthermore, in one embodiment, the step of obtaining baseline performance parameters when performing a grasping action includes: Receive real-time performance parameters within the sliding time window, where the sliding time window is the most recent M capture cycles, and M is a natural number between 20 and 100; The minimum value of the real-time performance parameters of the sliding time window is extracted as the baseline performance parameter.

[0008] In another implementation, the step of obtaining baseline performance parameters when performing a grabbing action includes: Obtain the initial performance parameters of the stacking device fixture when it performs a gripping action in its initial working state; It receives environmental parameter data from environmental sensors and material aging indicators from the fixture itself; Based on environmental parameter data and material aging indicators, the initial baseline performance parameters are dynamically adjusted to obtain the baseline performance parameters.

[0009] More specifically, in some implementation schemes, the steps for determining the type of performance degradation of the fixture based on baseline performance parameters and real-time performance parameters include: Calculate the deviation between baseline performance parameters and real-time performance parameters; When the real-time performance parameters continue to increase over N consecutive cycles, and the deviation between the real-time performance parameters and the baseline performance parameters exceeds the first threshold over N consecutive cycles, it is determined that the fixture is in a performance decline trend. When the fixture is in a performance decline trend, if the real-time performance parameter of the (N+1)th cycle is less than the real-time performance parameter of the Nth cycle, a peeling event is confirmed. Calculate the spalling risk index based on spalling events; Based on the peeling risk index and real-time performance parameters, determine whether the performance degradation type of the fixture includes brittle film peeling.

[0010] Preferably, the step of calculating the spalling risk index based on spalling events includes: Obtain the number of times X and the frequency H of the peeling event; The spalling risk index is calculated based on the number of occurrences X and the frequency of occurrence H, where the spalling risk index = X × H.

[0011] Based on the above, this application further proposes that, after determining whether the performance degradation type of the fixture includes brittle film peeling according to the peeling risk index and real-time performance parameters, the following steps are included: When the performance degradation type of the fixture includes brittle film peeling, and the peeling risk index exceeds the preset high risk index and the real-time performance parameters exceed the preset threshold, it is determined that the fixture has a high risk of brittle film peeling. When the performance degradation type of the fixture includes brittle film peeling, and the peeling risk index exceeds the preset high risk index and the real-time performance parameters are lower than the preset threshold, it is determined that the fixture has a medium brittle film peeling risk.

[0012] In some preferred embodiments, the step of determining the type of performance degradation of the fixture based on baseline performance parameters and real-time performance parameters includes: Calculate the deviation between baseline performance parameters and real-time performance parameters; When the deviation exceeds the preset disturbance threshold, the fluctuation event count increases by 1; When two consecutive deviations have opposite signs, the fluctuation direction change count increases by 1; The particle activity index is calculated based on the number of fluctuation events and the number of changes in fluctuation direction. The particle activity index = (number of fluctuation events / 20) + (number of changes in fluctuation direction / 20). Based on the particulate activity index, determine whether the performance degradation type of the fixture includes the risk of environmental particulate interference.

[0013] Based on the above, this application further proposes that the steps for determining the type of performance degradation of the fixture based on baseline performance parameters and real-time performance parameters include: Calculate the deviation between baseline performance parameters and real-time performance parameters; When the real-time performance parameters continue to increase over Z consecutive cycles, and the rate of change of the deviation exceeds the preset rate of change, the performance degradation type of the fixture is determined to be gradual performance decline caused by glue accumulation.

[0014] To improve the solution, the steps for determining the risk level of the stacking device fixture performance based on the type of fixture performance degradation include: When the performance degradation type is either the risk of peeling off a highly brittle adhesive film from the fixture or the risk of peeling off a moderately brittle adhesive film, and the accumulation of adhesive leads to gradual performance degradation, the risk level is determined to be high. When the performance degradation type only involves the risk of peeling off the brittle adhesive film from the fixture or only involves the gradual performance degradation caused by adhesive accumulation, the level is determined to be medium. When the performance degradation type only involves the risk of environmental particulate interference or the risk of medium-brittle film peeling, the risk level is determined to be low.

[0015] Secondly, this application also discloses a stacking device control system for an automated dispensing production line, the system comprising: The real-time performance parameter acquisition module is used to acquire the performance parameters of the stacking device fixture when performing the gripping action during production operation, and obtain the real-time performance parameters, including vacuum build-up time and / or negative pressure leakage rate. The benchmark performance parameter acquisition module is used to acquire benchmark performance parameters when performing the grabbing action; The performance degradation type determination module is used to determine the type of performance degradation of the fixture based on the baseline performance parameters and real-time performance parameters. The risk level determination module is used to determine the risk level of the stacking device fixture performance based on the type of fixture performance degradation. The early warning module is used to issue early warning information based on the risk level and provide guidance information for maintenance operations.

[0016] This application discloses a control method for a stacking device in an automated dispensing production line. By acquiring performance parameters such as vacuum build-up time and / or negative pressure leakage rate during real-time gripping actions of the stacking device's clamps during production operation and comparing them with benchmark performance parameters, the method can dynamically determine the type of performance degradation of the clamps. Based on this, the risk level of the clamp performance is determined according to the degradation type, and timely warning information is issued, along with specific maintenance operation guidance. This method effectively solves the problem in existing technologies of being unable to predict and prevent the gradual failure process caused by clamp contamination. Through continuous monitoring and intelligent analysis of performance parameters, this application can identify the trend of decreasing clamp adsorption force in advance, avoiding serious consequences such as workpiece falling, scrapping, equipment failure, and production line downtime. Compared to traditional passive fault detection, this application achieves predictive maintenance, significantly improving the operating efficiency and reliability of automated production lines, and reducing production costs and maintenance risks.

[0017] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0018] The accompanying drawings are used to provide a further understanding of the technical solutions of this application and constitute a part of the specification. They are used together with the embodiments of this application to explain the technical solutions of this application and do not constitute a limitation on the technical solutions of this application.

[0019] Figure 1This is a flowchart illustrating a stacking device control method for an automated glue dispensing production line, provided as an embodiment of this application. Detailed Implementation

[0020] To make the objectives, technical methods, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0021] It should be noted that the meaning of "multiple" (or "more than") in the description of the embodiments of this application refers to two or more, and "greater than," "less than," "exceeding," etc. are understood to exclude the number itself, while "above," "below," "within," etc. are understood to include the number itself. If "first," "second," etc. are described, they are only for the purpose of distinguishing technical features and should not be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated or the order of the technical features indicated.

[0022] See Figure 1 , Figure 1 This is a flowchart illustrating a stacking device control method for an automated dispensing production line according to an embodiment of this application. The stacking device control method for an automated dispensing production line provided in this application includes, but is not limited to, steps S110 to S150, which will be described in detail below.

[0023] S110. During production operation, the performance parameters of the stacking device clamps when performing gripping actions are acquired in real time to obtain real-time performance parameters, including vacuum build-up time and / or negative pressure leakage rate. S120. Obtain the baseline performance parameters when performing the grabbing action; S130. Determine the type of performance degradation of the fixture based on the baseline performance parameters and real-time performance parameters; S140. Determine the risk level of the stacking device fixture performance based on the type of fixture performance degradation. S150. Based on the risk level, issue early warning information and provide guidance information for maintenance operations.

[0024] To better understand the stacking device control method for automated glue dispensing production lines proposed in this application, it is necessary to explain some key terms involved.

[0025] "Stacking device" generally refers to a system consisting of an industrial robot and its end effector (i.e., "gripper") used to grasp, transport, and stack workpieces. In this application, "gripper" specifically refers to a vacuum suction cup or other similar adsorption device used to adsorb workpieces. "Performance parameters" are key indicators for measuring the working state of the gripper. For example, "vacuum build-up time" refers to the time required for the vacuum suction cup to reach a preset negative pressure value from the start of evacuation; a longer time generally indicates lower adsorption efficiency. "Negative pressure leakage rate" refers to the rate at which the negative pressure value of the vacuum system decreases over time during adsorption; a faster rate generally indicates poorer suction cup sealing. These parameters directly reflect the gripper's adsorption capacity and sealing performance. "Real-time performance parameters" refer to performance data collected in real-time by sensors or a control system during production operation. "Benchmark performance parameters" refer to the performance reference values ​​of the gripper under normal, healthy working conditions, used for comparison with real-time performance parameters. "Performance degradation type" refers to the specific cause or mode of performance degradation of the gripper, such as adhesive film peeling, environmental particulate interference, or adhesive accumulation. "Risk level" is a quantitative assessment of the degree of performance degradation of the fixture and its impact on production, and is usually divided into different levels such as high, medium and low.

[0026] The method described in this application first requires real-time acquisition of performance parameters during the gripping action of the stacking device fixture during production operation, thus obtaining real-time performance parameters. These performance parameters may include vacuum build-up time and / or negative pressure leakage rate. For example, this can be achieved by installing a pressure sensor and a timer on the vacuum line of the vacuum suction cup. When the suction cup begins its gripping action, the timer starts, and the pressure sensor monitors the pressure changes within the vacuum chamber. When the pressure reaches a preset negative pressure threshold, the timer stops, and the vacuum build-up time is recorded. Simultaneously, after adsorption stabilizes, the pressure drop rate can be continuously monitored to calculate the negative pressure leakage rate. Another implementation method is to utilize existing sensor data integrated into the stacking device control system. Many industrial robot systems inherently possess the ability to monitor the vacuum system status, and this data can be directly read as real-time performance parameters through a programming interface.

[0027] Next, it is necessary to obtain the baseline performance parameters for the gripping action. There are several ways to obtain these baseline performance parameters. One method is to conduct a series of standard gripping tests during the initial use of the fixture, or after thorough cleaning and maintenance. In these tests, the fixture is in its optimal working condition, and the performance parameters recorded at this time can be used as the initial baseline performance parameters. For example, during the production line commissioning phase, the stacking device can grip a standard workpiece 100 times, and the vacuum build-up time and negative pressure leakage rate during each grip can be recorded. The average or optimal value of these data can then be used as the baseline performance parameter. Another method is to dynamically determine the baseline performance parameters during production operation by statistically analyzing historical data. For example, a sliding time window can be set to continuously collect real-time performance parameters over a recent period, and a statistic (such as the minimum, average, or median) can be extracted as the current baseline performance parameter.

[0028] Subsequently, based on baseline and real-time performance parameters, the type of performance degradation of the fixture is determined. This typically involves analyzing the differences between real-time and baseline performance parameters. For example, if real-time performance parameters (such as vacuum build-up time) are significantly greater than baseline performance parameters, or if the negative pressure leakage rate is significantly accelerated, it indicates that the fixture performance may be degraded. The specific judgment logic can be based on preset thresholds or statistical models. For example, when the deviation between real-time and baseline performance parameters exceeds a certain preset threshold, the system can initially determine that performance degradation exists. To more accurately identify the type of degradation, the patterns of these deviations can be further analyzed. For example, a continuous, gradual performance decline may indicate glue accumulation, while a sudden, large-scale performance fluctuation may indicate environmental particulate interference or adhesive film peeling.

[0029] After determining the type of performance degradation of the fixture, it is necessary to determine the risk level of the stacking device fixture performance based on this type. Different types of degradation have varying degrees of impact on production, and therefore the corresponding risk levels should also differ. For example, minor environmental particulate interference may only result in low risk, while severe glue accumulation or brittle film peeling may lead to high risk. The risk level can be determined based on a predefined set of rules or a risk assessment model. For example, a rule can be set: if progressive performance degradation due to glue accumulation is detected, the risk level is medium; if a high risk of brittle film peeling is also detected, the risk level is increased to high.

[0030] Finally, based on the determined risk level, the system will issue a warning and provide guidance on maintenance operations. Warnings can be issued in various forms, such as audible and visual alarms, control interface pop-ups, SMS notifications, or email notifications, so that operators or maintenance personnel can pay attention promptly. Simultaneously, the system will provide targeted maintenance guidance based on the specific type of degradation and risk level. For example, if the risk level is low and the degradation type is environmental particulate interference, the guidance might be "Check the cleanliness of the production environment and clean the particles on the suction cup surface"; if the risk level is high and the degradation type is gradual performance degradation due to glue accumulation, the guidance might be "Immediately stop the machine and thoroughly clean or replace the suction cup."

[0031] The overall working principle of this application is to proactively manage the performance of stacking device fixtures by establishing a closed-loop monitoring, diagnosis, early warning and guidance mechanism.

[0032] During production, the system continuously and in real-time acquires the fixture's performance parameters, such as vacuum build-up time and negative pressure leakage rate. This real-time data is sent to the analysis module and compared with pre-set or dynamically updated benchmark performance parameters. By calculating deviations and analyzing trends and patterns, the system can intelligently determine the type of performance degradation that the fixture may be experiencing, such as gradual degradation due to glue accumulation, sudden decline due to brittle adhesive film peeling, or fluctuations caused by environmental particulate interference.

[0033] Once a degradation type is identified, the system determines the corresponding risk level based on its potential impact on production line stability and product quality. For example, minor performance fluctuations may be assessed as low risk, while severe degradation that could cause workpieces to fall is assessed as high risk. Ultimately, based on the determined risk level, the system immediately issues corresponding warning information and notifies relevant personnel via human-machine interface, SMS, or email. More importantly, the system also provides detailed and actionable maintenance guidance information based on the specific degradation type and risk level, such as suggestions to clean suction cups, inspect vacuum lines, adjust environmental parameters, or replace worn parts.

[0034] Through this series of steps, this application can transform traditional passive fault response into proactive predictive maintenance, thereby effectively avoiding production interruptions, product scrap, and equipment damage caused by fixture performance degradation, and significantly improving the operating efficiency and reliability of automated dispensing production lines.

[0035] This application, by monitoring the performance parameters of the fixture in real time and combining them with benchmark parameters for intelligent analysis, can identify the trend and specific type of performance degradation of the fixture in advance. For example, when the vacuum build-up time continues to increase or the negative pressure leakage rate gradually accelerates, this application can determine the gradual performance degradation caused by glue accumulation and issue an early warning before the workpiece falls.

[0036] Furthermore, this application not only issues early warnings but also provides specific maintenance operation guidance based on the type of degradation. This enables maintenance personnel to intervene in a targeted manner, thereby eliminating potential faults in their infancy. This shift from "remedial action" to "prevention" significantly improves the stability and production efficiency of automated production lines, reduces unplanned downtime and material losses, and brings significant economic benefits to enterprises.

[0037] In some embodiments, the step of obtaining the baseline performance parameters when performing the grasping action includes: Receive real-time performance parameters within a sliding time window, wherein the sliding time window is the most recent M capture cycles, and M is a natural number from 20 to 100; The minimum value of the real-time performance parameters of the sliding time window is extracted as the baseline performance parameter.

[0038] Specifically, a sliding time window refers to a dynamically updated dataset containing real-time performance parameters acquired over the most recent M gripping cycles. M is a natural number between 20 and 100, chosen to balance the real-time nature and stability of the baseline parameters. A smaller M value allows the baseline parameters to respond more quickly to performance changes but may be more sensitive to short-term fluctuations; a larger M value provides a more stable baseline but may have a slower response time. In practical applications, the value of M can be adjusted based on specific production line characteristics, fixture type, and the required response speed to performance changes. Real-time performance parameters can be understood as performance data measured during production operations when the stacking device fixture performs gripping actions, such as vacuum build-up time or negative pressure leakage rate. Further, the minimum value of all real-time performance parameters within this sliding time window is extracted as the baseline performance parameter. The purpose is that, over a period of time, the optimal performance of the fixture typically corresponds to the minimum value of the performance parameter (e.g., the shortest vacuum build-up time or the lowest negative pressure leakage rate). By selecting the minimum value as the benchmark, it can be ensured that the established benchmark parameters represent the optimal performance level that the fixture can achieve within that time window, thus providing a strict and representative reference point for assessing whether the current real-time performance has deteriorated.

[0039] The above technical solution significantly improves the accuracy and robustness of benchmark performance parameters. Compared to methods using fixed or initial benchmark parameters, the solution in this application allows benchmark parameters to dynamically adapt to the actual operating conditions of the production line, thereby more accurately reflecting the true performance level of the fixture. This helps reduce misjudgments caused by inaccurate benchmark parameters, such as incorrectly identifying normal performance fluctuations as degradation, or failing to detect true performance decline in a timely manner. By providing a more representative benchmark, the solution in this application can more effectively support the judgment of fixture performance degradation types and the determination of risk levels, thereby improving the overall reliability and maintenance efficiency of the control method for stacking devices in automated dispensing production lines. In some embodiments, the step of obtaining the baseline performance parameters when performing the grasping action includes: Obtain the initial performance parameters of the stacking device fixture when it performs a gripping action in its initial working state; It receives environmental parameter data from environmental sensors and material aging indicators from the fixture itself; Based on the environmental parameter data and the material aging index, the initial baseline performance parameters are dynamically adjusted to obtain the baseline performance parameters.

[0040] Specifically, initial performance parameters refer to the performance parameters measured by the stacking device fixture under optimal working conditions (either brand new or after calibration) through a series of gripping actions, such as the average or stable value of vacuum build-up time and / or negative pressure leakage rate. These parameters represent the performance baseline of the fixture under ideal conditions. Environmental parameter data can be understood as external environmental factors affecting fixture performance, such as temperature, humidity, and air pressure in the production workshop. This data can be collected in real time by environmental sensors deployed on the production line. Material aging indicators refer to parameters reflecting the physical or chemical changes of materials in key fixture components (such as suction cups and seals) over time, such as changes in the material's elastic modulus, hardness, and fatigue cycles. These indicators can be estimated using sensors inside the fixture or through a preset lifespan model. In practical applications, dynamically adjusting the initial baseline performance parameters refers to correcting the initial performance parameters based on the received environmental parameter data and material aging indicators using a preset algorithm or model, so that they more accurately reflect the actual baseline performance of the fixture under the current environment and aging conditions. For example, when the ambient temperature rises, the vacuum build-up time of the fixture may shorten, and the baseline performance parameters should be adjusted accordingly; when the material aging degree increases, the negative pressure leakage rate may accelerate, and the baseline performance parameters should also be corrected. The purpose is to ensure the accuracy and real-time nature of the baseline performance parameters, thereby providing a reliable basis for subsequent performance degradation judgment.

[0041] Through the above technical solution, this application can provide a more accurate and real-time benchmark performance parameter. Compared with fixed or simply averaged benchmark parameters, dynamically adjusted benchmark parameters can fully consider fluctuations in the production environment and the natural aging process of fixture materials, thereby significantly improving the accuracy of judging the type of fixture performance degradation. This accuracy enables the system to identify potential performance problems earlier and more accurately, such as performance degradation caused by brittle adhesive film peeling, environmental particulate interference, or adhesive accumulation, thus enabling more timely early warning information and providing targeted maintenance operation guidance. Therefore, it can not only effectively avoid production interruptions and product quality problems caused by misjudgment or delayed judgment, but also extend the service life of fixtures, reduce maintenance costs, and improve the overall operating efficiency and reliability of automated dispensing production lines.

[0042] In some embodiments, the step of determining the type of fixture performance degradation based on baseline performance parameters and real-time performance parameters specifically includes: Calculate the deviation between the baseline performance parameters and the real-time performance parameters; When the real-time performance parameter continues to increase over N consecutive cycles, and the deviation between the real-time performance parameter and the benchmark performance parameter exceeds a first threshold over N consecutive cycles, it is determined that the fixture is in a performance decline trend. When the fixture is in a performance decline trend, if the real-time performance parameter of the (N+1)th cycle is less than the real-time performance parameter of the Nth cycle, a peeling event is confirmed. Calculate the peeling risk index based on the peeling events; Based on the peeling risk index and the real-time performance parameters, determine whether the performance degradation type of the fixture includes brittle film peeling.

[0043] Specifically, the deviation refers to the difference between the real-time performance parameter and the baseline performance parameter, which reflects the degree of change in fixture performance relative to the ideal state or historical best state. Calculating this deviation helps quantify the performance deviation. Here, N in "N consecutive cycles" is a preset natural number, such as 5, 10, or 20, to ensure that the observed performance changes are not random fluctuations but have a continuous trend. When the real-time performance parameter continuously increases over N consecutive cycles, and its deviation from the baseline performance parameter exceeds a preset first threshold, it indicates that the fixture performance is undergoing a gradual decline. The first threshold can be set based on actual production experience or experimental data to define what degree of deviation is considered a significant indicator of performance degradation.

[0044] Furthermore, once the fixture is determined to be in a performance decline trend, if the real-time performance parameters of the next (N+1)th cycle are actually lower than those of the Nth cycle, a spalling event can be confirmed. This phenomenon is usually due to the sudden, partial or complete spalling of the brittle adhesive film on the fixture surface after long-term performance degradation (e.g., weakened adhesion, prolonged vacuum build-up time), resulting in an abnormal improvement in performance parameters within a short period (e.g., a sudden shortening of vacuum build-up time, a sudden decrease in negative pressure leakage rate). However, this improvement is temporary and accompanied by potential risks. Therefore, a spalling risk index can be calculated based on the confirmed spalling events. This index aims to quantify the severity and potential hazards of spalling events, and its calculation method can comprehensively consider factors such as the number and frequency of spalling events. Finally, by combining the calculated spalling risk index with the current real-time performance parameters, it can be determined whether the fixture's performance degradation type includes brittle adhesive film spalling. For example, when the spalling risk index is high and the real-time performance parameters are within a specific range, the specific degradation mode of brittle adhesive film spalling can be identified more accurately.

[0045] Through the above technical solution, this application overcomes the shortcomings of existing technologies in identifying specific performance degradation types, especially brittle film peeling. By continuously monitoring real-time performance parameters, analyzing trends, and capturing characteristic fluctuations in peeling events, this solution can identify the critical degradation mode of brittle film peeling in a timely and accurate manner. This not only improves the accuracy and specificity of judging the performance degradation type of the stacking device fixture, avoiding production interruptions or product quality problems caused by misjudgment or delayed judgment, but also provides more precise guidance information for subsequent maintenance operations, thereby effectively extending the service life of the fixture, reducing maintenance costs, and ensuring the stable and efficient operation of the automated dispensing production line.

[0046] Specifically, the steps for calculating the spalling risk index based on spalling events include: Obtain the number of times X and the frequency H of the peeling event; The peeling risk index is calculated based on the number of occurrences X and the frequency of occurrence H, wherein the peeling risk index = X × H.

[0047] The number of peeling events, X, refers to the total number of peeling events detected within a specific monitoring period. For example, a time window can be set within which peeling events are statistically analyzed. The frequency of occurrence, H, refers to the frequency of peeling events within a specific monitoring period. It can be understood as the ratio of the number of peeling events, X, to the total number of grasping actions during that monitoring period, or to the total monitoring time. The peeling risk index is calculated by multiplying the number of occurrences, X, by the frequency of occurrence, H. Its purpose is to provide a comprehensive quantitative indicator to assess the severity and potential impact of brittle film peeling.

[0048] The above technical solution provides a more accurate and comprehensive spalling risk assessment mechanism. This spalling risk index considers not only the actual number of spalling events but also their frequency, making the assessment of brittle film spalling risk more scientific and reliable. This helps avoid false alarms or omissions caused by errors in judging based on a single indicator, thereby improving the accuracy of determining the type of performance degradation of stacking device clamps and providing solid data support for timely maintenance measures.

[0049] In some embodiments, after the step of determining whether the performance degradation type of the fixture includes brittle film peeling based on the peeling risk index and real-time performance parameters, the method further includes: When the performance degradation type of the fixture includes brittle film peeling, and the peeling risk index exceeds the preset high risk index and the real-time performance parameters exceed the preset threshold, it is determined that the fixture has a high risk of brittle film peeling. When the performance degradation type of the fixture includes brittle film peeling, and the peeling risk index exceeds the preset high risk index and the real-time performance parameters are lower than the preset threshold, it is determined that the fixture has a medium brittle film peeling risk.

[0050] Specifically, the aforementioned preset high-risk index refers to the critical value of the peeling risk index used to define high-risk peeling events. Its setting aims to identify peeling situations that may seriously affect production line operation. When the calculated peeling risk index is higher than this preset high-risk index, it indicates that the frequency or severity of peeling events has reached a high level. The preset threshold refers to the critical value used to evaluate real-time performance parameters. Its purpose is to combine the peeling risk index to more comprehensively assess the severity of brittle film peeling. For example, when real-time performance parameters (such as vacuum build-up time or negative pressure leakage rate) exceed this preset threshold, it usually means that the gripping performance of the fixture has been significantly affected, potentially indicating a more serious peeling problem. By combining the peeling risk index and real-time performance parameters, the risk of brittle film peeling can be more finely classified.

[0051] Through the above technical solution, this application can achieve a refined classification of the risk of brittle adhesive film peeling, overcoming the limitations of merely determining whether peeling exists. This classification mechanism makes maintenance guidance more targeted. For example, for a high risk of brittle adhesive film peeling, an immediate shutdown for maintenance can be arranged; for a medium risk of brittle adhesive film peeling, inspection can be scheduled for the next maintenance window. This not only improves maintenance efficiency and reduces the risk of unplanned downtime, but also more effectively extends the service life of the fixtures, ensuring the stable operation of the automated dispensing production line. In some preferred embodiments, a preset high-risk index is set to 50, and a preset threshold (e.g., vacuum build-up time) is set to 2 seconds. Specifically, when the system detects that the performance degradation of the fixture includes brittle film peeling, if the calculated peeling risk index is 60 (exceeding the preset high-risk index of 50) and the real-time performance parameter (e.g., vacuum build-up time) is 2.5 seconds (exceeding the preset threshold of 2 seconds), the system will determine that the fixture has a high risk of brittle film peeling. In this case, the system will issue an emergency warning and recommend immediate shutdown for inspection or replacement of the fixture. As a specific implementation, if the peeling risk index is 60 (exceeding the preset high-risk index of 50), but the real-time performance parameter (e.g., vacuum build-up time) is 1.8 seconds (below the preset threshold of 2 seconds), the system will determine that the fixture has a medium risk of brittle film peeling. In this case, the system will issue a moderate warning and recommend a detailed inspection and maintenance of the fixture in the next planned maintenance cycle to prevent further escalation of the risk. In this way, differentiated maintenance strategies can be adopted according to the actual severity of the risk, thereby optimizing the operating efficiency and safety of the production line.

[0052] In some embodiments, the step of determining the type of performance degradation of the fixture based on the baseline performance parameters and the real-time performance parameters includes: Calculate the deviation between baseline performance parameters and real-time performance parameters; When the deviation exceeds the preset disturbance threshold, the fluctuation event count increases by 1; When two consecutive deviations have opposite signs, the fluctuation direction change count increases by 1; The particle activity index is calculated based on the number of fluctuation events and the number of changes in fluctuation direction. The particle activity index = (number of fluctuation events / 20) + (number of changes in fluctuation direction / 20). Based on the particulate activity index, determine whether the performance degradation type of the fixture includes the risk of environmental particulate interference.

[0053] Specifically, calculating the deviation between baseline performance parameters and real-time performance parameters involves comparing the real-time acquired performance parameters with pre-set or dynamically adjusted baseline performance parameters to quantify the difference between the two. This deviation value intuitively reflects the degree to which the current fixture performance deviates from the ideal state. The preset disturbance threshold can be understood as a critical value for judging whether performance fluctuations are significant. Its setting is usually based on historical data analysis, fixture design specifications, or expert experience, aiming to filter out normal, harmless performance fluctuations.

[0054] When the deviation exceeds a preset disturbance threshold, the fluctuation event count is incremented by 1. The fluctuation event count is used to accumulate and record the number of times performance parameters fluctuate significantly, reflecting the frequency of fixture performance disturbances. When two consecutive deviations have opposite signs, the fluctuation direction change count is incremented by 1. The fluctuation direction change count is used to measure the frequency of changes in the direction of performance parameter fluctuations, such as from positive deviation to negative deviation, or from negative deviation to positive deviation. This frequent change in direction is a typical characteristic of performance instability caused by environmental particulate interference.

[0055] Furthermore, the particulate activity index is calculated based on the count of fluctuation events and the count of changes in fluctuation direction. This index comprehensively reflects the frequency and irregularity of performance fluctuations by weighted summation of these two counts (e.g., normalization by dividing by 20 respectively). Dividing by 20 can be understood as a normalization process, ensuring the index value is within a manageable range, facilitating subsequent risk assessment. Ultimately, based on the calculated particulate activity index, it can be determined whether the fixture's performance degradation type includes environmental particulate interference risk. For example, when the particulate activity index exceeds a certain preset threshold, environmental particulate interference risk can be considered to exist.

[0056] Through the above technical solution, this application can accurately identify the risk of environmental particulate interference in the performance degradation types of stacking device fixtures. Compared with relying solely on trend changes or threshold judgments of performance parameters, this solution, by introducing fluctuation event counting and fluctuation direction change counting, and constructing a particulate activity index, can more sensitively capture subtle and irregular performance fluctuations caused by environmental particles. This not only improves the accuracy of performance degradation type judgment and avoids misjudging environmental particulate interference as other degradation types, but also makes early warning information and maintenance operation guidance more targeted. For example, when the risk of environmental particulate interference is identified, timely recommendations can be made for environmental cleaning or filter replacement, thereby effectively preventing more serious performance problems caused by particulate accumulation, extending fixture life, and ensuring the stable operation of automated dispensing production lines.

[0057] In some embodiments, the step of determining the type of performance degradation of the fixture includes: Calculate the deviation between the baseline performance parameters and the real-time performance parameters; If the real-time performance parameter continues to increase over Z consecutive cycles, and the rate of change of the deviation exceeds a preset rate of change, the performance degradation type of the fixture is determined to be gradual performance decline caused by glue accumulation.

[0058] Specifically, calculating the deviation between baseline performance parameters and real-time performance parameters refers to quantifying the degree to which fixture performance deviates from its normal state by comparing the currently acquired real-time performance parameters with preset or dynamically updated baseline performance parameters. This deviation can intuitively reflect the changing trend of fixture performance. Performance parameters may include vacuum build-up time or negative pressure leakage rate. For example, a longer vacuum build-up time or a faster negative pressure leakage rate usually indicates a decrease in fixture performance.

[0059] Furthermore, when the real-time performance parameters continuously increase over Z consecutive cycles, and the rate of change of the deviation exceeds a preset rate of change, it can be determined that the performance degradation of the fixture is due to gradual performance decline caused by glue accumulation. Here, "Z consecutive cycles" refers to continuous monitoring of the fixture's gripping action within a certain time window to ensure that the observed performance changes are not random fluctuations but rather exhibit a continuous trend. Z can be a preset integer, such as 5, 10, or 20, the choice of which depends on the specific characteristics of the production line and the sensitivity requirements to performance changes. A continuous increase in real-time performance parameters, such as a continuously prolonged vacuum build-up time or a continuously accelerating negative pressure leakage rate, is usually due to the gradual accumulation of glue or other foreign matter on or inside the fixture surface, leading to a decrease in its adsorption capacity or a deterioration in its sealing performance. The rate of change of deviation refers to the rate at which the deviation between the real-time performance parameters and the baseline performance parameters increases over consecutive cycles. When this rate of change exceeds a preset rate of change, it indicates that the rate of performance degradation has reached a level requiring attention, which is highly consistent with the gradual degradation pattern caused by glue accumulation. The preset rate of change is an empirical value or a threshold determined through historical data analysis, used to distinguish between normal fluctuations and actual performance degradation trends.

[0060] This application's solution effectively identifies the gradual performance degradation of fixtures caused by glue accumulation by introducing monitoring of the continuous increasing trend of real-time performance parameters and the rate of change of deviation. Traditional performance degradation judgment may only focus on whether instantaneous performance parameters exceed a threshold, ignoring the long-term, slow performance decline process. However, glue accumulation is usually a gradual process, and its impact on fixture performance is not immediate but gradually manifests over time. By observing the continuous increase of real-time performance parameters over Z consecutive cycles, short-term random fluctuations can be eliminated, confirming the trend of performance decline. Simultaneously, monitoring the rate of change of deviation further quantifies the severity and speed of this decline trend. When this rate of change exceeds a preset threshold, the gradual degradation pattern unique to glue accumulation can be accurately captured, thus providing an early warning before significant performance deterioration. This mechanism enables the system to diagnose glue accumulation problems earlier and more accurately, avoiding production interruptions or product quality issues caused by failure to detect them in time.

[0061] In some preferred embodiments, a specific example is given below. Suppose that in an automated dispensing production line, the vacuum build-up time of the stacking device fixture is one of its key performance parameters. Under normal operating conditions, the vacuum build-up time is typically around 0.5 seconds. As production progresses, adhesive may gradually accumulate on the fixture surface, leading to a decrease in adsorption efficiency and a corresponding increase in vacuum build-up time.

[0062] The system first acquires the vacuum settling time during the gripper's grasping action as a real-time performance parameter. Simultaneously, the system maintains a baseline performance parameter, determined, for example, by the minimum value within a sliding time window or an initial calibration value. During a given monitoring period, the system calculates the deviation between the real-time vacuum settling time and the baseline vacuum settling time.

[0063] Specifically, assuming the system is set to Z = 10 cycles with a preset change rate of 0.01 seconds per cycle, over 10 consecutive capture cycles, the system detected that the vacuum setup time gradually increased from 0.5 seconds to 0.6 seconds, with each cycle's vacuum setup time being longer than the previous one. Simultaneously, the deviation between the real-time performance parameters and the baseline performance parameters was calculated, and it was found that the rate of change of this deviation (e.g., an increase of 0.015 seconds per cycle) exceeded the preset change rate of 0.01 seconds per cycle.

[0064] Based on these observations, the system determines that the fixture's performance degradation is due to gradual performance decline caused by glue accumulation. Subsequently, the system determines the corresponding risk level based on this degradation type and issues early warning information, such as prompting operators to check for glue residue on the fixture surface and suggesting cleaning or replacement. This early warning mechanism effectively prevents gripping failures or product damage caused by glue accumulation, thereby ensuring stable production line operation and product quality.

[0065] In some embodiments, the step of determining the risk level of the stacking device fixture performance based on the type of fixture performance degradation includes: When the performance degradation type is either a high risk of peeling off the brittle adhesive film of the fixture or a medium risk of peeling off the adhesive film, and the accumulation of adhesive leads to gradual performance degradation, the risk level is determined to be high. When the performance degradation type only presents the risk of brittle adhesive film peeling from the fixture or only presents the gradual performance degradation caused by adhesive accumulation, the level is determined to be medium. When the performance degradation type only presents the risk of environmental particulate interference or the risk of medium-brittle film peeling, the risk level is determined to be low.

[0066] Specifically, performance degradation types refer to the specific problems that may exist in the fixture, identified through comparative analysis of real-time performance parameters and baseline performance parameters. These include risks such as brittle adhesive film peeling (including high-brittleness and medium-brittleness adhesive film peeling risks), gradual performance degradation due to adhesive accumulation, and environmental particulate interference risks. The determination of risk levels aims to prioritize these degradation types so that the system can adopt different early warning and maintenance strategies based on the severity of the risk. High-risk levels typically indicate that the fixture performance is severely compromised, potentially leading to production interruptions or product quality issues, requiring immediate intervention. Medium-risk levels indicate significant fixture performance problems; while not leading to immediate production stoppages, the risk may escalate if not addressed promptly, requiring planned maintenance. Low-risk levels indicate minor fixture performance anomalies, potentially having a small impact on production, but still requiring continuous monitoring and can be addressed within the regular maintenance cycle. In practical applications, the determination of the above risk levels is based on a logical combination judgment of multiple degradation types. For example, when both the risk of highly brittle adhesive film peeling (or moderately brittle adhesive film peeling) and the risk of gradual performance degradation due to adhesive accumulation exist simultaneously, the system will classify it as high risk because the combination of these two problems could lead to rapid failure of the fixture function. If only one of the more severe degradation types exists, such as only the risk of highly brittle adhesive film peeling or only the risk of gradual performance degradation due to adhesive accumulation, it will be classified as medium risk, indicating that the problem is serious but has not yet reached the most urgent state. When only the risk of environmental particulate interference or only the risk of moderately brittle adhesive film peeling exists, because these problems usually have a smaller impact or develop more slowly, they will be classified as low risk.

[0067] This application's solution establishes a risk assessment mechanism based on combinations of performance degradation types, enabling a more comprehensive and accurate reflection of the actual operating status and potential failure risks of stacking device fixtures. When the system identifies multiple performance degradation types, such as the simultaneous presence of brittle adhesive film peeling risk and glue accumulation leading to gradual performance degradation, this indicates that the fixture may face a compound failure, the impact of which on production will be far greater than a single failure. By mapping these compound failures to higher risk levels, the system can promptly identify potential serious problems. Furthermore, for single degradation types of varying severity, such as high-brittle adhesive film peeling risk versus medium-brittle adhesive film peeling risk, and environmental particulate interference risk, this solution also provides differentiated risk level classifications, allowing early warning and maintenance guidance to more accurately match actual needs. This tiered approach avoids generalizing about all degradation types, thereby improving the precision and practicality of risk assessment.

[0068] In some preferred embodiments, a specific example is given below. Suppose that in an automated dispensing production line, after the stacking device fixture has been running continuously for a period of time, the system, through real-time monitoring and analysis, identifies that the fixture simultaneously exhibits two types of performance degradation: "high risk of brittle adhesive film peeling" and "gradual performance degradation due to adhesive accumulation." According to the risk level determination method of this application, since both types of degradation exist simultaneously, the system will determine the performance risk level of the fixture to be "high." At this time, the early warning module will immediately issue a high-level warning message and provide guidance for emergency maintenance operations, such as suggesting immediate shutdown for inspection and replacement of the fixture or thorough cleaning. Alternatively, if the system only identifies a "risk of environmental particulate interference" with the fixture, then according to the scheme of this application, the risk level will be determined to be "low." In this case, the system may issue a low-level warning and suggest cleaning or inspection during the next planned maintenance, without immediately interrupting production. Yet another example, if the system identifies only a "risk of high brittle adhesive film peeling" with the fixture, the risk level will be determined to be "medium." At this point, the system will issue a medium-level warning and recommend scheduling maintenance in the short term, such as inspection and handling after the next production batch, to prevent further escalation of the risk. These examples clearly demonstrate how this application can flexibly and accurately determine the risk level based on different types and combinations of performance degradation, thereby providing strong assurance for the stable operation of the production line.

[0069] In automated production lines, especially in processes involving precision adhesive coating, the efficiency and reliability of material handling are crucial. Stacking devices, typically composed of industrial robots and specialized grippers, are responsible for the orderly transfer and stacking of processed workpieces. However, in actual production, slight adhesive overflow may occur during the application process, known as "overflow." When the grippers of the stacking device, particularly vacuum suction cups, contact and pick up these workpieces with uncured overflow, some adhesive transfers to the contact surface of the suction cup. With hundreds or thousands of repeated gripping actions, this residual adhesive gradually accumulates on the suction cup surface and begins to slowly cure upon contact with air, forming a thin, viscous film. The formation of this film fundamentally alters the physical properties of the suction cup surface. For vacuum suction cups, their working principle relies on the sealed space formed between the suction cup edge and the workpiece surface, using negative pressure generated by vacuuming to adsorb the workpiece. The accumulated adhesive film disrupts the smoothness and flexibility of the suction cup lip, causing tiny leakage gaps when it adheres to the workpiece surface. This makes it take longer for the vacuum system to build up sufficient negative pressure, or in some cases, it may not be able to reach the preset stable negative pressure value at all. Eventually, when glue contamination accumulates to a certain level, the suction force of the suction cup will drop to a critical point. At this point, during the high-speed movement or turning of the robotic arm, due to inertial forces, the poorly held workpiece may experience a slight positional shift, or even be directly thrown off. Once the workpiece falls, it will not only render the workpiece unusable, but may also damage other workpieces already placed on the tray below, or even fall into the precision mechanical structure of the equipment, causing more serious equipment failures and leading to the shutdown of the entire production line. Traditional control systems respond very passively to such problems. They can only detect the final failure result, such as "grabbing failure" or "no workpiece at the placement position," by checking the presence or absence of sensors installed at the end of the robotic arm or at the tray station, but they cannot predict or prevent this gradual failure process caused by fixture contamination.

[0070] In this regard, a specific embodiment of this application also discloses a stacking device control system for an automated dispensing production line, the system comprising: The real-time performance parameter acquisition module is used to acquire the performance parameters of the stacking device fixture when it performs the gripping action during production operation, and obtain the real-time performance parameters, wherein the performance parameters include vacuum build-up time and / or negative pressure leakage rate. The benchmark performance parameter acquisition module is used to acquire benchmark performance parameters when performing the grabbing action; A performance degradation type determination module is used to determine the performance degradation type of the fixture based on the baseline performance parameters and the real-time performance parameters. The risk level determination module is used to determine the risk level of the stacking device fixture performance based on the type of performance degradation of the fixture. The early warning module is used to issue early warning information based on the risk level and provide guidance information for maintenance operations.

[0071] The stacking device control system of this application, through its modular design, achieves comprehensive, real-time monitoring and intelligent diagnosis of the performance of the stacking device fixtures in an automated dispensing production line. This system can proactively identify the performance degradation trends and specific types of the fixtures, and issue timely warnings and provide maintenance guidance based on the risk level. This effectively avoids production accidents and downtime caused by fixture performance degradation, significantly improving the stability and efficiency of the automated production line.

[0072] In some embodiments of this application, the various modules in the stacking device control system described above can be implemented in a variety of ways.

[0073] The real-time performance parameter acquisition module of this application can be configured to connect to the sensor system (e.g., pressure sensor, timer, etc.) of the stacking device via a data interface to collect performance data such as vacuum build-up time and negative pressure leakage rate during the gripping action of the fixture in real time. This module can be a standalone hardware unit, such as a data acquisition card or embedded controller, or it can be a software submodule integrated into the main controller of the stacking device, responsible for data reading, preliminary processing, and transmission.

[0074] The benchmark performance parameter acquisition module can be configured to store preset benchmark performance parameters or dynamically generate benchmark parameters by performing statistical analysis on historical performance data. For example, the module can include a storage unit to save the benchmark values ​​obtained during initial calibration; or it can be a processing unit that periodically receives real-time performance parameters and calculates and updates the benchmark performance parameters according to preset algorithms (such as average value extraction, minimum value extraction, etc.).

[0075] The performance degradation type determination module can be configured to receive real-time performance parameters and baseline performance parameters, and execute a preset diagnostic algorithm. This module can be a software program running on a central processing unit or a dedicated digital signal processor. Through comparison, trend analysis, pattern recognition, and other technologies, it identifies the types of performance degradation that may exist in the fixture, such as glue accumulation, brittle adhesive film peeling, or environmental particulate interference.

[0076] The risk level determination module can be configured to calculate and determine the risk level of fixture performance based on the type of performance degradation and according to preset risk assessment rules or models. This module can be a decision support system containing a rule base or machine learning model to map different combinations of degradation types to corresponding risk levels (e.g., high, medium, low).

[0077] The early warning module can be configured to generate and issue corresponding early warning information based on a determined risk level, and provide guidance information for maintenance operations. This module may include a user interface display, an audible and visual alarm, and a network communication interface (for sending SMS or emails) to promptly convey early warning information and maintenance recommendations to operators or maintenance teams. The guidance information for maintenance operations can be a pre-set maintenance manual database that automatically retrieves and displays corresponding maintenance steps based on risk level and degradation type.

[0078] The stacking device control system of this application represents a significant advancement over existing technologies. Traditional automated production line control systems typically only detect problems after a final failure, such as a workpiece falling or a gripping failure, resulting in a passive and unpreventative response. For example, when glue accumulation causes a decrease in suction cup adhesion, existing systems only trigger an alarm when the workpiece actually falls, at which point the damage has already occurred. In contrast, the system of this application continuously monitors the performance parameters of the fixture through a real-time performance parameter acquisition module, with reference provided by a baseline performance parameter acquisition module. An intelligent analysis module, specifically a performance degradation type judgment module, performs this analysis, enabling early identification of the fixture's performance degradation trend and specific type. For instance, when the vacuum build-up time continuously increases or the negative pressure leakage rate gradually accelerates, the system of this application can determine the gradual performance degradation caused by glue accumulation and issue a warning before the workpiece falls. Furthermore, the system of this application not only issues warnings but also provides specific maintenance operation guidance based on the degradation type. This allows maintenance personnel to intervene in a targeted manner, thus eliminating potential faults in their early stages. This shift from "post-event remediation" to "pre-event prevention" has greatly improved the stability and production efficiency of automated production lines, reduced unplanned downtime and material losses, and brought significant economic benefits to enterprises.

[0079] The foregoing has provided a detailed description of the preferred embodiments of this application. However, this application is not limited to the above-described embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of this application. All such equivalent modifications or substitutions are included within the scope defined in this application.

Claims

1. A method for controlling a stacking device in an automated dispensing production line, characterized in that, Includes the following steps: During production operation, the performance parameters of the stacking device clamps performing gripping actions are acquired in real time to obtain real-time performance parameters, including vacuum build-up time and / or negative pressure leakage rate. Obtain baseline performance parameters when performing the grabbing action; Based on the baseline performance parameters and the real-time performance parameters, determine the type of performance degradation of the fixture; The risk level of the stacking device fixture performance is determined based on the type of performance degradation of the fixture. Based on the risk level, an early warning message is issued, and guidance information for maintenance operations is provided.

2. The method according to claim 1, characterized in that, The steps for obtaining the baseline performance parameters when performing the crawling action include: Receive real-time performance parameters within a sliding time window, wherein the sliding time window is the most recent M capture cycles, and M is a natural number from 20 to 100; The minimum value of the real-time performance parameters of the sliding time window is extracted as the baseline performance parameter.

3. The method according to claim 1, characterized in that, The steps for obtaining the baseline performance parameters when performing the crawling action include: Obtain the initial performance parameters of the stacking device fixture when it performs a gripping action in its initial working state; It receives environmental parameter data from environmental sensors and material aging indicators from the fixture itself; Based on the environmental parameter data and the material aging index, the initial baseline performance parameters are dynamically adjusted to obtain the baseline performance parameters.

4. The method according to claim 1, characterized in that, The step of determining the performance degradation type of the fixture based on the baseline performance parameters and the real-time performance parameters includes: Calculate the deviation between the baseline performance parameters and the real-time performance parameters; When the real-time performance parameter continues to increase over N consecutive cycles, and the deviation between the real-time performance parameter and the benchmark performance parameter exceeds a first threshold over N consecutive cycles, it is determined that the fixture is in a performance decline trend. When the fixture is in a performance decline trend, if the real-time performance parameter of the (N+1)th cycle is less than the real-time performance parameter of the Nth cycle, a peeling event is confirmed. Calculate the peeling risk index based on the peeling events; Based on the peeling risk index and the real-time performance parameters, determine whether the performance degradation type of the fixture includes brittle film peeling.

5. The method according to claim 4, characterized in that, The step of calculating the peeling risk index based on the peeling event includes: Obtain the number of times X and the frequency H of the peeling event; The peeling risk index is calculated based on the number of occurrences X and the frequency of occurrence H, wherein the peeling risk index = X × H.

6. The method according to claim 4, characterized in that, The step of determining whether the performance degradation type of the fixture includes brittle film peeling based on the peeling risk index and the real-time performance parameters includes: When the performance degradation type of the fixture includes brittle film peeling, and the peeling risk index exceeds the preset high risk index and the real-time performance parameters exceed the preset threshold, it is determined that the fixture has a high risk of brittle film peeling. When the performance degradation type of the fixture includes brittle film peeling, and the peeling risk index exceeds the preset high risk index and the real-time performance parameters are lower than the preset threshold, it is determined that the fixture has a medium brittle film peeling risk.

7. The method according to claim 1, characterized in that, The step of determining the performance degradation type of the fixture based on the baseline performance parameters and the real-time performance parameters includes: Calculate the deviation between the baseline performance parameters and the real-time performance parameters; When the deviation exceeds a preset disturbance threshold, the fluctuation event count increases by 1; When two consecutive deviations have opposite signs, the fluctuation direction change count increases by 1; The particle activity index is calculated based on the fluctuation event count and the fluctuation direction change count, wherein the particle activity index = (fluctuation event count / 20) + (fluctuation direction change count / 20); Based on the particle activity index, determine whether the performance degradation type of the fixture includes the risk of environmental particle interference.

8. The method according to claim 1, characterized in that, The step of determining the performance degradation type of the fixture based on the baseline performance parameters and the real-time performance parameters includes: Calculate the deviation between the baseline performance parameters and the real-time performance parameters; If the real-time performance parameter continues to increase over Z consecutive cycles, and the rate of change of the deviation exceeds a preset rate of change, the performance degradation type of the fixture is determined to be gradual performance decline caused by glue accumulation.

9. The method according to claim 1, characterized in that, The step of determining the risk level of the stacking device fixture performance based on the type of performance degradation of the fixture includes: When the performance degradation type is either a high risk of peeling off the brittle adhesive film of the fixture or a medium risk of peeling off the adhesive film, and the accumulation of adhesive leads to gradual performance degradation, the risk level is determined to be high. When the performance degradation type only presents the risk of brittle adhesive film peeling from the fixture or only presents the gradual performance degradation caused by adhesive accumulation, the level is determined to be medium. When the performance degradation type only presents the risk of environmental particulate interference or the risk of medium-brittle film peeling, the risk level is determined to be low.

10. A stacking device control system for an automated dispensing production line, characterized in that, The system includes: The real-time performance parameter acquisition module is used to acquire the performance parameters of the stacking device fixture when it performs the gripping action during production operation, and obtain the real-time performance parameters, wherein the performance parameters include vacuum build-up time and / or negative pressure leakage rate. The benchmark performance parameter acquisition module is used to acquire benchmark performance parameters when performing the grabbing action; A performance degradation type determination module is used to determine the performance degradation type of the fixture based on the baseline performance parameters and the real-time performance parameters. The risk level determination module is used to determine the risk level of the stacking device fixture performance based on the type of performance degradation of the fixture. The early warning module is used to issue early warning information based on the risk level and provide guidance information for maintenance operations.

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