A method and system for upgrading and optimizing production parameters of glue needle gun

Abstract

The present invention proposes a method and system for upgrading and optimizing the production parameters of a glue needle gun. Belonging to the technical field of glue needle guns, the method includes: setting a parameter monitoring system on the glue needle gun production line, monitoring the production parameter data in real time through the parameter monitoring system, and transmitting the monitoring data to the central control system in real time; the central control system performs real-time analysis on the received monitoring data and identifies abnormal conditions in the production process; generates corresponding adjustment instructions based on the abnormal conditions, and automatically adjusts the production parameters of the glue needle gun through the automatic adjustment system according to the generated adjustment instructions. Through a multi-level parameter monitoring system and a sensor network, real-time monitoring of production parameters and instant transmission of data are achieved, which helps to promptly discover any deviations in the production process, thereby quickly responding and taking measures, reducing product quality problems and reduced production efficiency caused by abnormal parameters.

Description

Technical Field

[0001] The invention proposes a method and system for upgrading and optimizing production parameters of a glue needle gun, belonging to the technical field of glue needle guns. Background Art

[0002] With the rapid development of the manufacturing industry and the intensification of market competition, enterprises have increasingly higher requirements for production efficiency and product quality. As a common industrial sewing equipment, the setting of production parameters of glue needle guns directly affects the quality and production efficiency of products. Traditional methods of adjusting production parameters of glue needle guns often rely on manual experience or fixed adjustment schemes. This method is not only inefficient, but also difficult to adapt to various changes in the production process, resulting in unstable product quality and low production efficiency.

[0003] In order to solve the above problems, in recent years, with the development of automation technology and intelligent control technology, more and more companies have begun to explore the upgrade and optimization of glue needle gun production parameters by introducing parameter monitoring systems and automatic adjustment systems. This upgrade optimization method monitors production parameter data in real time and automatically adjusts production parameters according to the monitoring results to improve production efficiency and product quality.

[0004] However, the existing methods for upgrading and optimizing the production parameters of glue needle guns still have some shortcomings. First, due to the complexity and variability of the production process, a single monitoring system and adjustment system may not be able to fully and accurately reflect the production status, resulting in poor adjustment effects. Secondly, traditional adjustment methods often lack adaptive capabilities and cannot be flexibly adjusted according to real-time changes in the production process. Finally, existing optimization methods often only focus on the adjustment of production parameters, while ignoring the optimization and upgrading of the entire production system. Summary of the invention

[0005] The present invention provides a method and system for upgrading and optimizing the production parameters of a glue needle gun, which is used to solve the problems mentioned in the above background technology:

[0006] The present invention proposes a method for upgrading and optimizing production parameters of a glue needle gun, the method comprising:

[0007] S1. A parameter monitoring system is set up on the glue needle gun production line, and the production parameter data is monitored in real time through the parameter monitoring system, and the monitoring data is transmitted to the central control system in real time;

[0008] S2. The central control system performs real-time analysis on the received monitoring data and identifies abnormal conditions in the production process;

[0009] S3, generating corresponding adjustment instructions based on the abnormal situation, and automatically adjusting the production parameters of the glue needle gun through the automatic adjustment system according to the generated adjustment instructions;

[0010] S4. After the automatic adjustment system executes the adjustment command, the adjusted production parameters are again monitored in real time through the parameter monitoring system, and the monitoring data is fed back to the central control system for verification and optimization.

[0011] Furthermore, the S1 includes:

[0012] S11. Construct a multi-level parameter monitoring system in the glue needle gun production line, wherein the multi-level parameter monitoring system includes various sensors and a data processing module;

[0013] S12, constructing a touch sensor network based on various sensors, collecting production parameter data in real time through the sensor network, and transmitting the collected production parameter data to the data processing module;

[0014] S13, the data processing module automatically identifies and classifies the received data of different sensor types through intelligent algorithms and machine learning technology, and numbers and groups the data according to the sensor type;

[0015] S14, based on the data pipeline, pre-processing the numbered and classified data respectively, and compressing the pre-processed monitoring data respectively through a compression algorithm;

[0016] S15. The pre-processed monitoring data are transmitted to the central control system through a multi-channel transmission protocol. During the transmission process, the bandwidth allocation is dynamically adjusted according to the real-time network status and data volume through a dynamic bandwidth allocation strategy.

[0017] Furthermore, the S2 includes:

[0018] S21, the central control system decompresses the received compressed monitoring data respectively, and stores the decompressed monitoring data in different storage spaces respectively;

[0019] S22, extracting features from the data in the storage space, and classifying the monitoring data based on a machine learning algorithm to identify different types of abnormal situations, and assigning processing priorities to the abnormal situations based on the severity and impact of the abnormal situations;

[0020] S23. Analyze the root causes of the identified abnormalities, detect the associations between various abnormalities, and analyze the influence relationships between different production parameters;

[0021] S24. Generate an intelligent control strategy based on the abnormal situation and root cause analysis results, and perform simulation tests on the intelligent control strategy.

[0022] Furthermore, the S3 includes:

[0023] S31. The central control system receives the intelligent control strategy, decodes the received intelligent control strategy, and clarifies the target adjustment value of each production parameter;

[0024] S32. Evaluate the risk of the target adjustment value based on historical data and the current production environment; assign a risk level to each risk based on the evaluation results and predict the likelihood of its occurrence;

[0025] S33. Generate specific adjustment instructions based on the target adjustment value and the risk assessment result; prioritize the generated adjustment instructions based on multiple factors;

[0026] S34. Before executing the adjustment instruction, the data of the production line is collected in real time through the parameter monitoring system, and compared with the target value for verification. If there is a large difference between the real-time data and the target value, an alarm is triggered; and the adjustment instruction is fine-tuned according to the verification result of the real-time data;

[0027] S35. Send the adjustment instruction to the automatic adjustment system, and execute the adjustment operation based on the multi-stage adjustment strategy. During the adjustment process, feedback data is collected in real time, and the feedback data is learned through the machine learning algorithm to continuously optimize the adjustment strategy and algorithm.

[0028] Furthermore, the S4 includes:

[0029] S41. During the production process, multiple key parameters are monitored in real time and their change data are collected. By comparing the target parameter values ​​with the actual monitored parameter values, it is verified whether the expected adjustment effect has been achieved;

[0030] S42. Use statistical tools to conduct multivariate analysis on the collected data to determine the interactions and mutual influences between the parameters. Use advanced anomaly detection algorithms to conduct in-depth analysis of real-time monitoring data to identify anomalies in the production process.

[0031] S43, classifying and recording the detected abnormalities, performing preliminary analysis on the recorded abnormalities based on a data mining algorithm, and compiling the mined analysis results into a report;

[0032] S44, evaluating the effect of the current adjustment strategy, and iteratively optimizing the adjustment strategy according to the evaluation result;

[0033] S45. Apply the iterated adjustment strategy to actual production, evaluate its effect through real-time monitoring and data verification, and regularly evaluate the overall health of the production system;

[0034] S46. Develop a preventive maintenance plan based on the evaluation results, perform corresponding maintenance tasks according to the maintenance plan, and track and record the maintenance results.

[0035] The present invention provides a glue needle gun production parameter upgrading and optimization system, the system comprising:

[0036] Real-time monitoring module: a parameter monitoring system is set up on the glue needle gun production line, the parameter monitoring system is used to monitor the production parameter data in real time, and the monitoring data is transmitted to the central control system in real time;

[0037] Real-time analysis module: The central control system performs real-time analysis on the received monitoring data and identifies abnormal situations in the production process.

[0038] Automatic adjustment module: Generates corresponding adjustment instructions based on abnormal situations. According to the generated adjustment instructions, the automatic adjustment system automatically adjusts the production parameters of the glue needle gun;

[0039] Verification and optimization module: After the automatic adjustment system executes the adjustment command, the adjusted production parameters are again monitored in real time through the parameter monitoring system, and the monitoring data is fed back to the central control system for verification and optimization.

[0040] Furthermore, the real-time monitoring module includes:

[0041] Building modules: Building a multi-level parameter monitoring system in the glue needle gun production line, the multi-level parameter monitoring system includes various sensors and data processing modules;

[0042] Transmission module: Build a sensor network based on various sensors, collect production parameter data in real time through the sensor network, and transmit the collected production parameter data to the data processing module;

[0043] Classification module: The data processing module automatically identifies and classifies the received data of different sensor types through intelligent algorithms and machine learning technology, and numbers and groups the data according to the sensor type;

[0044] Preprocessing module: Based on the data pipeline, the numbered and classified data are preprocessed separately, and the preprocessed monitoring data are compressed separately through the compression algorithm;

[0045] Adjustment module: The pre-processed monitoring data is transmitted to the central control system through a multi-channel transmission protocol; during the transmission process, the bandwidth allocation is dynamically adjusted according to the real-time network status and data volume through a dynamic bandwidth allocation strategy.

[0046] Furthermore, the real-time analysis module includes:

[0047] Decompression module: The central control system decompresses the received compressed monitoring data and stores the decompressed monitoring data in different storage spaces;

[0048] Abnormal identification module: extracts features from the data in the storage space, and classifies the monitoring data based on machine learning algorithms to identify different types of abnormal situations, and prioritizes the abnormal situations based on their severity and impact;

[0049] Root cause analysis module: Root cause analysis of identified abnormalities, correlation detection between various abnormalities, and analysis of the impact relationship between different production parameters;

[0050] Simulation module: Generates intelligent control strategies based on abnormal conditions and root cause analysis results, and performs simulation tests on the intelligent control strategies.

[0051] Furthermore, the automatic adjustment module includes:

[0052] Decoding module: The central control system receives the intelligent control strategy, decodes the received intelligent control strategy, and clarifies the target adjustment value of each production parameter;

[0053] Assessment module: Evaluate the risk of target adjustment value based on historical data and current production environment; assign a risk level to each risk based on the assessment results and predict the possibility of its occurrence;

[0054] Instruction generation module: Generates specific adjustment instructions based on target adjustment values ​​and risk assessment results; prioritizes the generated adjustment instructions based on multiple factors;

[0055] Comparison and verification module: Before executing the adjustment instruction, the parameter monitoring system collects the data of the production line in real time and compares and verifies it with the target value. If there is a large difference between the real-time data and the target value, an alarm is triggered; and the adjustment instruction is fine-tuned according to the verification result of the real-time data;

[0056] Operation execution module: sends adjustment instructions to the automatic adjustment system and executes adjustment operations based on multi-stage adjustment strategies. During the adjustment process, feedback data is collected in real time, and the feedback data is learned through machine learning algorithms to continuously optimize adjustment strategies and algorithms.

[0057] Furthermore, the verification optimization module includes:

[0058] Change data collection module: During the production process, multiple key parameters are monitored in real time and their change data are collected. By comparing the target parameter value with the actual monitored parameter value, it is verified whether the expected adjustment effect has been achieved;

[0059] Variable analysis module: Use statistical tools to conduct multivariate analysis on the collected data, determine the interaction and mutual influence between various parameters, and use advanced anomaly detection algorithms to conduct in-depth analysis of real-time monitoring data to identify abnormal points in the production process;

[0060] Classification and recording module: classifies and records detected anomalies, performs preliminary analysis on recorded anomalies based on data mining algorithms, and compiles the mined analysis results into reports;

[0061] Iterative optimization module: evaluates the effectiveness of the current adjustment strategy and iteratively optimizes the adjustment strategy based on the evaluation results;

[0062] Application module: Apply the iterated adjustment strategy to actual production, evaluate its effect through real-time monitoring and data verification, and regularly evaluate the overall health of the production system;

[0063] Maintenance module: formulate preventive maintenance plan based on the evaluation results, perform corresponding maintenance tasks according to the maintenance plan, and track and record the maintenance results.

[0064] Beneficial effects of the present invention: through the multi-level parameter monitoring system and sensor network, real-time monitoring of production parameters and instant transmission of data are realized, which helps to timely discover any deviations in the production process, so as to respond quickly and take measures, reducing product quality problems and reduced production efficiency caused by abnormal parameters; using machine learning and intelligent algorithms, the system can automatically identify abnormal situations in the production process, which not only improves the accuracy of abnormal identification, but also can automatically divide the processing priority according to the severity of the abnormality, making problem solving more efficient and orderly; intelligent control strategy combined with historical data analysis provides a scientific basis for the adjustment of production parameters and reduces the risks brought by the adjustment. Through risk assessment and priority sorting, the effectiveness and safety of adjustment instructions are ensured, and the fine-tuning mechanism further guarantees the accuracy of the adjustment; the system continuously learns and optimizes the adjustment strategy through a feedback mechanism. This closed-loop control strategy enables the production process to be continuously improved and improves product quality and production efficiency. The application of machine learning algorithms enables the system to learn from feedback data and adapt to changes in the production environment. Through regular assessments of the health of the production system and preventive maintenance plans based on data analysis, it can effectively predict and prevent equipment failures, reduce unplanned downtime, extend equipment life, and ensure production continuity and stability. The system can not only automatically process and optimize production parameters, but also generate analysis reports to provide decision support for management. By classifying and recording abnormal situations and conducting preliminary analysis, it helps management understand the problems and their root causes in production, and provides data support for future production planning and strategic decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 It is a step diagram of the method of the present invention;

[0066] Figure 2 This is a system module diagram of the present invention. DETAILED DESCRIPTION

[0067] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0068] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. The embodiments described are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0069] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0070] One embodiment of the present invention, as Figure 1 As shown, a method for upgrading and optimizing production parameters of a glue needle gun comprises:

[0071] S1. A parameter monitoring system is set up on the glue needle gun production line to monitor production parameter data in real time through the parameter monitoring system. The production parameters include needle length, needle speed and glue amount, and the monitoring data is transmitted to the central control system in real time;

[0072] S2. The central control system performs real-time analysis on the received monitoring data and identifies abnormal conditions in the production process, including needle length error, needle speed fluctuation, and uneven glue amount;

[0073] S3. Generate corresponding adjustment instructions based on abnormal situations. According to the generated adjustment instructions, the automatic adjustment system automatically adjusts the production parameters of the glue needle gun; such as adjusting the needle length through the servo motor, adjusting the needle speed through the inverter, and adjusting the glue amount through the precision metering pump.

[0074] S4. After the automatic adjustment system executes the adjustment command, the adjusted production parameters are again monitored in real time through the parameter monitoring system, and the monitoring data is fed back to the central control system for verification and optimization. If the adjustment effect does not meet expectations, the adjustment command is regenerated until the optimal production state is achieved.

[0075] The working principle of the above technical solution is as follows: a parameter monitoring system is set up on the glue needle gun production line, which is responsible for real-time monitoring of key production parameters such as needle length, needle speed and glue amount. These monitoring data are transmitted to the central control system in real time to provide a basis for subsequent data analysis and adjustment; after receiving the monitoring data, the central control system will immediately perform real-time analysis. By analyzing the fluctuations and trends of the data and comparing with historical data, the system can identify abnormal conditions in the production process, such as needle length errors, needle speed fluctuations and uneven glue amount; once an abnormal condition is identified, the central control system will generate corresponding adjustment instructions based on the preset adjustment algorithms and strategies. These instructions will be sent to the automatic adjustment system, and the system will adjust the needle length through the servo motor, the needle speed through the inverter, and the glue amount through the precision metering pump according to the instructions. This automatic adjustment ensures the accuracy and efficiency of the adjustment; after the automatic adjustment system executes the adjustment instructions, the parameter monitoring system will again monitor the adjusted production parameters in real time. The monitoring data is again transmitted to the central control system to verify the adjustment effect. If the adjustment effect does not meet expectations, the central control system will re-analyze the data, generate new adjustment instructions, and continue to make adjustments through the automatic adjustment system until the optimal production state is achieved.

[0076] The effects of the above technical solution are as follows: by real-time monitoring and adjustment of production parameters, abnormal situations in the production process can be discovered and resolved in a timely manner, production interruptions and downtime can be reduced, and production efficiency can be improved; by accurately controlling key production parameters such as needle length, needle speed and glue quantity, the consistency and stability of the product can be ensured, the defective rate can be reduced, and product quality and customer satisfaction can be improved; by optimizing production parameters, the waste of raw materials and energy consumption can be reduced, and production costs can be reduced. At the same time, reducing the defective rate also avoids the additional costs caused by product rework or returns; the automatic adjustment system can quickly adjust production parameters according to different production needs and product specifications, and improve production flexibility and adaptability; the technical solution combines advanced monitoring, analysis and automatic adjustment technologies to realize the intelligent and automated production process, reduce dependence on manual labor, and improve production efficiency and management convenience; real-time monitoring and recording of production parameter data provide data support for the traceability and analysis of the production process, which helps to discover and solve problems in a timely manner and improve the level of production management; through precise control of production parameters, production accidents and safety hazards caused by improper parameter settings or excessive fluctuations are avoided, and production safety is improved.

[0077] In one embodiment of the present invention, the S1 includes:

[0078] S11. Construct a multi-level parameter monitoring system in the glue needle gun production line, wherein the multi-level parameter monitoring system includes various sensors and data processing modules, wherein the various sensors include a laser rangefinder, a high-speed encoder, and a high-precision flow sensor;

[0079] S12, constructing a touch sensor network based on various sensors, collecting production parameter data in real time through the sensor network, and transmitting the collected production parameter data to the data processing module;

[0080] S13, the data processing module automatically identifies and classifies the received data of different sensor types through intelligent algorithms and machine learning technology, and numbers and groups the data according to the sensor type;

[0081] S14, based on the data pipeline, pre-processing the numbered and classified data respectively, and compressing the pre-processed monitoring data respectively through a compression algorithm; the data pre-processing includes data cleaning and outlier removal;

[0082] S15. The pre-processed monitoring data are transmitted to the central control system through a multi-channel transmission protocol. During the transmission process, the bandwidth allocation is dynamically adjusted according to the real-time network status and data volume through a dynamic bandwidth allocation strategy.

[0083] The working principle of the above technical solution is as follows: In the glue needle gun production line, in order to comprehensively and accurately monitor the production parameters, a multi-level parameter monitoring system is first constructed. This system consists of various sensors and data processing modules. The sensors are selected according to the type of parameters to be monitored, such as laser rangefinders for measuring needle length, high-speed encoders for monitoring needle speed, and high-precision flow sensors for measuring glue volume; then, a touch sensor network is constructed based on these sensors. This network is responsible for real-time collection of data from each sensor, that is, production parameter data. The collected data will be transmitted to the data processing module in real time; in the data processing module, the received sensor data is first automatically identified and classified by intelligent algorithms and machine learning techniques. This is because there are various types of sensors on the production line, and the types of data collected are also different, so classification processing is required for subsequent analysis. At the same time, the data is numbered and grouped according to the type of sensor, so that the sensor and parameter type to which each data belongs can be accurately identified in subsequent processing; the classified data will enter the data preprocessing stage. This stage includes data cleaning and outlier removal. Data cleaning is to remove noise and invalid information in the data to ensure the accuracy and reliability of the data. The purpose of outlier elimination is to exclude abnormal data caused by equipment failure, operational errors, etc., to avoid interference with subsequent analysis and adjustment. The preprocessed data will be compressed by a compression algorithm to reduce the amount of data transmission and storage space occupied; finally, the preprocessed monitoring data will be transmitted to the central control system through a multi-channel transmission protocol. In this process, a dynamic bandwidth allocation strategy is adopted. This is because the network conditions and data volume may change in real time. If the bandwidth allocation is fixed, it may cause delays or losses in data transmission. The dynamic bandwidth allocation strategy can dynamically adjust the bandwidth allocation of each channel according to the real-time network conditions and data volume to ensure the real-time and integrity of the data.

[0084] The effect of the above technical solution is: by building a multi-level parameter monitoring system, using various sensors including laser rangefinders, high-speed encoders and high-precision flow sensors, the key parameters of the glue needle gun production line, such as needle length, needle speed and glue volume, can be fully and accurately monitored. This ensures the accuracy and reliability of the data and provides a solid foundation for subsequent production optimization; using intelligent algorithms and machine learning technology, the data processing module can automatically identify and classify data from different sensors, and perform numbering and grouping processing. This not only improves the data processing efficiency, but also enables the system to intelligently adapt to data of different types and sources, enhancing the flexibility and scalability of the system; through data preprocessing, such as data cleaning and outlier removal, noise and invalid information can be removed to improve the quality of data. At the same time, using compression algorithms to compress the preprocessed monitoring data can reduce the amount of data transmission and storage space occupied, reduce data transmission costs, and improve the real-time and reliability of data transmission; using multi-channel transmission protocols, the preprocessed monitoring data can be transmitted to the central control system separately. At the same time, through the dynamic bandwidth allocation strategy, the bandwidth allocation can be dynamically adjusted according to the real-time network status and data volume to ensure the real-time and integrity of the data. This avoids delays or losses in data transmission and improves the stability and reliability of the entire system; by real-time monitoring of production parameters and automatic adjustment of production parameters, abnormal situations in the production process can be discovered and resolved in a timely manner, production interruptions and downtime can be reduced, and production efficiency can be improved. At the same time, precise control of production parameters can ensure product consistency and stability, reduce defective rates, improve product quality and customer satisfaction; by optimizing production parameters and reducing raw material waste, production costs can be reduced. At the same time, reducing defective rates also avoids additional costs caused by product rework or returns; the entire system realizes the intelligence and automation of the production process, reduces dependence on manual labor, and improves production efficiency and management convenience. In addition, through real-time data monitoring and analysis, data support can be provided for production decisions, achieving more accurate production control and optimization.

[0085] In one embodiment of the present invention, the S2 includes:

[0086] S21, the central control system decompresses the received compressed monitoring data respectively, and stores the decompressed monitoring data in different storage spaces respectively;

[0087] S22, extracting features from the data in the storage space, and classifying the monitoring data based on a machine learning algorithm to identify different types of abnormalities, such as needle length error, needle speed fluctuation, and uneven glue amount; and prioritizing the abnormalities based on their severity and impact;

[0088] S23. Root cause analysis is performed on the identified abnormalities to explore possible causes of the abnormalities, such as mechanical failure, supply chain problems or operational errors, etc. Association detection is performed between various abnormalities, and the impact relationship between different production parameters is analyzed;

[0089] S24. Generate an intelligent control strategy based on the abnormal situation and root cause analysis results, and perform simulation tests on the intelligent control strategy.

[0090] The working principle of the above technical solution is as follows: when the central control system receives the compressed monitoring data sent from the data processing module, it will first perform a decompression operation to restore the original form of the data. The decompressed monitoring data will be stored in different storage spaces in the central control system for subsequent efficient data processing and analysis; after the data storage is completed, the central control system will perform feature extraction on the data in the storage space. Feature extraction is to extract representative information or patterns from the original data through algorithms, and these features can reflect the main characteristics and trends of the data. Then, based on the machine learning algorithm, the system will classify and identify these features to distinguish different types of abnormal situations, such as needle length error, needle speed fluctuation, and uneven glue amount. According to the severity of the abnormal situation and the potential impact on the production process, the system will also prioritize the abnormal situation to ensure that those problems with the greatest impact on production are dealt with first; after identifying the abnormal situation, the system will perform root cause analysis. Root cause analysis is the process of exploring the root cause of the abnormal situation, and possible causes include mechanical failure, supply chain problems, operational errors, etc. The system will perform correlation detection on various abnormal situations and analyze the influence relationship between different production parameters to find out the root cause that may cause the abnormal situation. This correlation analysis helps to better understand the problems in the production process and provides strong support for subsequent solution strategies; after the root cause analysis is completed, the central control system will generate intelligent control strategies based on the abnormal situation and the root cause analysis results. These strategies are solutions to specific abnormal situations, aiming to eliminate or mitigate the impact of abnormal situations by adjusting production parameters or taking other measures. In order to verify the effectiveness of the strategy, the system will conduct simulation tests on it. Simulation is to simulate the actual production process in a virtual environment and apply the generated intelligent control strategy to observe its effect. In this way, the system can predict the performance of the strategy in actual production and further optimize and adjust the strategy based on the test results.

[0091] The effects of the above technical solutions are as follows: the decompression process ensures the originality and integrity of the data, providing an accurate data basis for subsequent analysis. Feature extraction and classification recognition based on machine learning algorithms can efficiently and accurately identify different types of abnormalities, such as needle length error, needle speed fluctuation, and uneven glue amount, improving the accuracy and efficiency of fault detection. The processing priority is divided according to the severity and impact of the abnormal situation, ensuring that the problems with the greatest impact on production are dealt with first, so as to quickly restore the stability and efficiency of the production line. Root cause analysis of the identified abnormalities can deeply explore the root causes of the abnormalities, such as mechanical failures, supply chain problems, or operational errors, providing strong support for subsequent solution strategies. By associating detection and analyzing the influence relationship between different production parameters, a more comprehensive understanding of the problems in the production process can be achieved, which helps to formulate more effective solutions. Based on the abnormal situations and root cause analysis results, intelligent control strategies are generated, which can accurately control specific problems and help to quickly restore the normal operation of the production line. Simulation tests of intelligent control strategies can predict the effects of strategies without interfering with actual production, and further optimize and adjust them according to the test results to ensure that the best results can be achieved in actual applications. Through real-time monitoring, accurate identification, in-depth analysis and effective response to abnormal situations in the production process, the stability and reliability of the production line can be ensured, production interruptions and downtime can be reduced, and production efficiency can be improved. Precision control can ensure product consistency and stability, reduce defective rates, and improve product quality and customer satisfaction. Through rapid response and effective handling of abnormal situations, losses caused by production interruptions and downtime can be reduced, and production costs can be reduced. Precision control can reduce the waste of raw materials and energy consumption, further reducing production costs.

[0092] In one embodiment of the present invention, the S23 includes:

[0093] Based on the self-organizing map (SOM) algorithm, unsupervised learning is performed on abnormal data, similar abnormal data points are mapped to similar neurons, and an abnormal pattern diagram is obtained; and the abnormal pattern diagram is analyzed to identify different abnormal areas and abnormal types;

[0094] Constructing a complex network model containing multi-dimensional information, including production parameters, equipment status, and environmental factors;

[0095] Use network analysis algorithms (such as community detection and path analysis) to find key nodes and paths related to anomalies in complex networks and form multi-dimensional root cause hypotheses;

[0096] The real-time data stream processing system monitors and analyzes the real-time data in the production process. When a new anomaly is detected, the real-time data stream is used to trigger the verification process of the root cause hypothesis. The root cause hypothesis is verified by comparing the real-time data with the expected data.

[0097] The posterior probability of each root cause hypothesis is calculated through the Bayesian network model, and the root cause hypotheses are prioritized according to the posterior probability.

[0098] The working principle of the above technical solution is: unsupervised learning of abnormal data is performed using the self-organizing map (SOM) algorithm. The SOM algorithm is a neural network method that can map the data of the input space to a two-dimensional grid while maintaining the topological structure of the input data. In this process, similar abnormal data points are mapped to similar neurons, thereby forming different abnormal pattern areas on the grid. These areas represent different abnormal types and patterns. A complex network model is constructed based on multi-dimensional information such as production parameters, equipment status, and environmental factors. The nodes in this network model can represent different parameters or factors, while the edges represent the relationship between them. Through the complex network model, the interaction and dependency between various parameters and factors can be intuitively seen. In the network analysis stage, a community detection algorithm is used to identify the community structure in the network, which may represent different functional modules or abnormal root causes. At the same time, the path analysis algorithm is used to find key nodes and paths related to the abnormality in the network. These key nodes and paths may be the key factors or propagation paths that cause the abnormality. The real-time data in the production process is monitored and analyzed through the real-time data stream processing system. When a new abnormality is detected, the real-time data stream triggers the verification process of the root cause hypothesis. This verification process is done by comparing the real-time data with the expected data. If there is a significant difference between the real-time data and the expected data, it may mean that a certain root cause hypothesis is correct. Using the Bayesian network model, the posterior probability of each root cause hypothesis is calculated. Bayesian network is a method based on probabilistic graphical models, which can represent the probabilistic dependencies between variables. In root cause analysis, Bayesian networks can be used to represent the dependencies and probabilistic relationships between different root cause hypotheses. By calculating the posterior probability of each root cause hypothesis, the root cause hypotheses can be prioritized according to the probability size, thereby determining which root cause hypotheses should be prioritized.

[0099] The effect of the above technical solution is: using the self-organizing map (SOM) algorithm, the system can automatically map similar abnormal data points to similar neurons to form an intuitive abnormal pattern diagram. This greatly improves the recognition efficiency of abnormal patterns and reduces the time and cost of manual analysis. The analysis of the abnormal pattern diagram can further identify different abnormal areas and types, providing a solid foundation for subsequent root cause analysis. Constructing a complex network model containing multi-dimensional information such as production parameters, equipment status, and environmental factors can comprehensively consider the impact of various factors on abnormalities and avoid the limitations of single factor analysis. By finding key nodes and paths in complex networks through network analysis algorithms, the potential causes and propagation mechanisms of abnormalities can be revealed, providing strong support for the formulation of targeted solutions. By monitoring and analyzing real-time data in the production process through a real-time data stream processing system, new abnormalities can be discovered in a timely manner, and the verification process of the root cause hypothesis can be triggered using real-time data streams. This real-time verification mechanism ensures the timeliness and accuracy of abnormal processing. Comparing real-time data with expected data to verify the root cause hypothesis can objectively evaluate the validity of the hypothesis and avoid subjective assumptions and misleading. The Bayesian network model can represent the probabilistic dependency between different root cause hypotheses. By calculating the posterior probability of each root cause hypothesis, its probability can be objectively evaluated. Prioritizing the root cause hypotheses according to the posterior probability helps enterprises prioritize the root causes with greater probability and more serious impact, thereby quickly restoring production order and reducing losses. The entire technical solution combines a variety of advanced algorithms and technologies to achieve automatic learning of abnormal data, identification of abnormal patterns, comprehensive analysis of multi-dimensional information, monitoring and analysis of real-time data, and priority sorting of root cause hypotheses, greatly improving the level of intelligent exception handling. This intelligent processing method can reduce the burden of manual analysis, improve the efficiency and quality of exception handling, and provide a strong guarantee for enterprises to cope with complex and changing production environments.

[0100] In one embodiment of the present invention, the S24 includes:

[0101] The initial strategy is obtained through imitation learning and rule-based, and the control strategy is designed according to the initial strategy through reinforcement learning algorithms (such as deep Q network, policy gradient method); so that the strategy can learn and optimize in the interaction with the environment.

[0102] Build a simulation environment that integrates virtual and real life, combine the virtual model of the production process with the actual production environment, and simulate the implementation effect of the control strategy in the simulation environment;

[0103] Generate different candidate control strategies using a generative adversarial network (GAN), evaluate the performance of each candidate in a simulation environment, and select the best solution as the final control strategy.

[0104] When the production environment changes, the knowledge of existing control strategies is transferred to the new environment using the transfer learning algorithm, and some network layers are fine-tuned or retrained; so that the control strategies can quickly adapt to the new environment and conditions; and the swarm intelligence mechanism is introduced to enable multiple control strategies to run and compete in parallel in the simulation environment;

[0105] By observing and analyzing the performance of different strategies, we select the best performing strategies for iteration and improvement to form better and more adaptable regulation strategies.

[0106] The working principle of the above technical solution is: the initial control strategy is obtained through imitation learning (for example, learning from expert experience or historical data) and rule-based methods. Then, the control strategy is iteratively designed and optimized according to this initial strategy using reinforcement learning algorithms (such as deep Q network, policy gradient method, etc.). In this process, the control strategy will continuously interact with the environment and adjust its actions according to the feedback of the environment to maximize the cumulative reward or minimize the cumulative cost. In order to evaluate and optimize the control strategy, a simulation environment integrating virtual and real is constructed. This environment combines the virtual model of the production process with the actual production environment, and can simulate various situations in the actual production environment in the virtual environment. By simulating the implementation effect of the control strategy in this simulation environment, the performance of the control strategy can be preliminarily evaluated. In order to explore more control strategy possibilities, a generative adversarial network (GAN) is used to generate different control strategy candidate solutions. GAN consists of a generator and a discriminator. The generator is responsible for generating candidate strategies, while the discriminator is responsible for evaluating the quality of these candidate strategies. By continuously iteratively training GAN, more and more high-quality control strategy candidate solutions can be generated. The candidate strategies generated by GAN are actually simulated and tested in the simulation environment to evaluate their performance. By comparing the performance of different candidate strategies in the simulation environment, the candidate strategy with the best performance can be selected as the final control strategy. When the production environment changes, the knowledge of the existing control strategy is transferred to the new environment using the transfer learning algorithm. By fine-tuning or retraining some network layers, the control strategy can quickly adapt to the new environment and conditions. This method can make full use of existing knowledge and experience to accelerate the learning and optimization process of the control strategy in the new environment. In order to further improve the adaptability and robustness of the control strategy, a swarm intelligence mechanism is introduced. In the simulation environment, multiple control strategies are allowed to run and compete in parallel. By observing and analyzing the performance of different strategies, the best performing strategy can be selected for iteration and improvement. This swarm intelligence method can make full use of the advantages of multiple strategies to form a more excellent and adaptable control strategy.

[0107] The effect of the above technical solution is: through imitation learning and rule-based methods, an initial control strategy can be quickly obtained, which provides a good starting point for subsequent reinforcement learning. Using reinforcement learning algorithms, such as deep Q networks or policy gradient methods, the control strategy can be continuously designed and optimized to ensure that the strategy gradually learns and adapts in the interaction with the environment. The virtual model of the production process and the actual production environment are integrated, so that the implementation effect of the simulated control strategy in the simulation environment is closer to reality and the evaluation result is more reliable. The simulation environment provides a safe and controllable environment for testing and verifying the strategy, reducing the risks that may be brought about by trying new strategies directly in the real environment. The introduction of GAN provides a powerful tool for generating different control strategy candidates, which can explore more possibilities and increase the chance of finding better strategies. Evaluating candidate solutions in the simulation environment can quickly screen out the control strategy with the best performance and improve the efficiency of strategy design. When the production environment changes, the transfer learning algorithm can be used to transfer the knowledge of the existing control strategy to the new environment, greatly shortening the time for strategy learning and optimization in the new environment. Fine-tuning or retraining some network layers enables the control strategy to quickly adapt to the new environment and conditions and maintain the stability and efficiency of the production process. By running and competing multiple control strategies in parallel, we can fully utilize the advantages of swarm intelligence and discover better and more adaptable control strategies. Observing and analyzing the performance of different strategies provides rich data and experience for the iteration and improvement of strategies, further improving the performance and adaptability of strategies. Carefully designed and optimized control strategies can better cope with various challenges and changes in the production process and maintain the stability and continuity of the production process. By continuously iterating and improving control strategies, we can further improve the efficiency and quality of the production process and reduce production costs and waste.

[0108] In one embodiment of the present invention, S3 includes:

[0109] S31, the central control system receives the intelligent control strategy, decodes the received intelligent control strategy, and clarifies the target adjustment value of each production parameter (needle length, needle speed, glue amount);

[0110] S32. Evaluate the risk of target adjustment values ​​based on historical data and current production environment; for example, a needle length that is too short may result in insufficient product strength, a needle speed that is too fast may cause increased equipment wear, and uneven glue volume may result in unstable product quality. Based on the evaluation results, assign a risk level to each risk and predict its likelihood of occurrence;

[0111] S33. Generate specific adjustment instructions based on the target adjustment value and the risk assessment result; these instructions include the rotation angle of the servo motor, the frequency setting of the inverter, the flow control of the precision metering pump, etc. Prioritize the generated adjustment instructions based on multiple factors, including the urgency of the adjustment, the impact on the overall production, and the cost-effectiveness of the adjustment; for example, if the needle length error is too large and affects the product quality, the priority of the instruction will be relatively high.

[0112] S34. Before executing the adjustment instruction, the parameter monitoring system collects data from the production line in real time and compares it with the target value for verification. If there is a large difference between the real-time data and the target value, an alarm is triggered; and the adjustment instruction is fine-tuned based on the verification result of the real-time data; for example, if the real-time data shows that the needle speed is close to the target value, the system may reduce the adjustment range of the inverter.

[0113] S35. Send the adjustment instruction to the automatic adjustment system, and perform the adjustment operation based on the multi-stage adjustment strategy. The multi-stage adjustment strategy includes that after each stage, the system will evaluate the adjustment effect and decide whether to continue to the next stage or end the adjustment. During the adjustment process, feedback data is collected in real time, including adjustment speed, adjustment accuracy, product quality changes, etc. The feedback data is learned through machine learning algorithms to continuously optimize adjustment strategies and algorithms. For example, the system may adjust the risk assessment model or priority sorting algorithm based on feedback data.

[0114] The working principle of the above technical solution is as follows: after receiving the intelligent control strategies, the central control system first decodes these strategies to clarify the target adjustment values ​​of each production parameter (needle length, needle speed, glue amount). These target adjustment values ​​are set by the system to optimize the production process based on the previous abnormal detection and analysis results; next, the system evaluates the risks that may be caused by the target adjustment values ​​based on historical data and the current production environment. This evaluation is comprehensive and detailed, and it takes into account various possible factors, such as insufficient product strength due to too short needle length, increased equipment wear due to too fast needle speed, and product quality problems due to uneven glue amount. Based on these evaluations, the system assigns a risk level to each potential risk and predicts its possibility of occurrence; after clarifying the target adjustment value and evaluating the potential risks, the system begins to generate specific adjustment instructions. These instructions are for each control element on the production line, such as the rotation angle of the servo motor, the frequency setting of the inverter, the flow control of the precision metering pump, etc. At the same time, the system will prioritize the generated adjustment instructions based on multiple factors (such as the urgency of the adjustment, the impact on the overall production, and the cost-effectiveness of the adjustment). For example, if the needle length error is too large and has a serious impact on product quality, the priority of the instruction to adjust the needle length will be relatively high; before executing the adjustment instruction, the system will collect data on the production line in real time through the parameter monitoring system and compare and verify it with the previously set target value. If there is a large difference between the real-time data and the target value, the system will trigger an alarm and fine-tune the adjustment instruction based on the verification results of the real-time data. This fine-tuning is dynamic and flexible, which ensures the accuracy and effectiveness of the adjustment process; after the fine-tuning is completed, the system sends the adjustment instruction to the automatic adjustment system and starts to implement a multi-stage adjustment strategy. This strategy is implemented step by step. After each stage, the system will evaluate the adjustment effect and decide whether to continue to the next stage or end the adjustment. During the entire adjustment process, the system will collect feedback data in real time, including adjustment speed, adjustment accuracy, and product quality changes. The system learns this data through machine learning algorithms and continuously optimizes the risk assessment model, priority sorting algorithm, and the adjustment strategy itself.

[0115] The effect of the above technical solution is: by decoding the intelligent control strategy and clarifying the target adjustment value of each production parameter, the system can ensure the accuracy of the adjustment. This helps to optimize the production process and improve product quality. According to the comparison, verification and fine-tuning of real-time data, the system can flexibly adapt to changes in the production environment to ensure that the adjustment operation is more in line with actual production needs. Based on historical data and the current production environment, the target adjustment value is risk assessed, and the system can predict potential risks and assign risk levels to them, so as to take preventive measures in advance to reduce production risks. This risk assessment helps enterprises better understand potential problems in the production process and formulate corresponding response strategies. According to multiple factors (such as the urgency of the adjustment, the impact on the overall production, and the cost-effectiveness of the adjustment), the adjustment instructions are prioritized. The system can ensure that the problems that have the greatest impact on production are dealt with first, thereby improving production efficiency and response speed. This priority sorting helps enterprises to achieve optimal allocation of resources and ensure the smooth progress of the production process. During the adjustment process, feedback data is collected in real time, including adjustment speed, adjustment accuracy, product quality changes, etc. The system can timely understand the adjustment effect and make optimization adjustments based on the feedback data. By learning the feedback data through machine learning algorithms, the system can continuously optimize the adjustment strategy and algorithm to improve the adjustment accuracy and efficiency. Through precise adjustment, risk assessment and prevention, and real-time feedback and optimization, the system can ensure the stability and reliability of the production process and reduce the probability of production interruptions and failures. This helps to improve production efficiency and product quality, reduce production costs, and enhance the market competitiveness of enterprises. This technical solution not only realizes the intelligent regulation of the production process, but also has the ability of autonomous learning and optimization. The system can continuously optimize and adjust strategies and algorithms based on real-time data and feedback data to improve the accuracy and efficiency of intelligent decision-making. This intelligent decision-making and autonomous learning ability can help enterprises better respond to market changes and competitive challenges and achieve sustainable development.

[0116] In one embodiment of the present invention, the S4 includes:

[0117] S41. During the production process, multiple key parameters (such as needle length, needle speed, glue amount, etc.) are monitored in real time and their change data are collected. By comparing the target parameter value with the actual monitored parameter value, it is verified whether the expected adjustment effect has been achieved;

[0118] S42. Use statistical tools to conduct multivariate analysis on the collected data to determine the interactions and mutual influences between the parameters. Use advanced anomaly detection algorithms to conduct in-depth analysis of real-time monitoring data to identify anomalies in the production process.

[0119] S43, classifying and recording the detected abnormalities, performing preliminary analysis on the recorded abnormalities based on a data mining algorithm, and compiling the mined analysis results into a report;

[0120] S44. Evaluate the effectiveness of the current adjustment strategy, including the effect, efficiency and possible risks of the adjustment. According to the evaluation results, iteratively optimize the adjustment strategy, such as adjusting the target value of the parameter, optimizing the adjustment algorithm, adding new adjustment steps, etc.

[0121] S45. Apply the iterative adjustment strategy to actual production, evaluate its effect through real-time monitoring and data verification, and regularly evaluate the overall health of the production system, including equipment operating status, production efficiency, product quality and other aspects.

[0122] S46. According to the evaluation results, formulate a preventive maintenance plan, including equipment maintenance, spare parts replacement, system upgrade, etc., to prevent the occurrence of potential problems, perform corresponding maintenance tasks according to the maintenance plan, and track and record the maintenance results to ensure the stable operation of the system.

[0123] The working principle of the above technical solution is as follows: during the production process, the system monitors multiple key parameters (such as needle length, needle speed, glue amount, etc.) in real time and collects the change data of these parameters through sensors and other devices. The real-time collected data will be compared with the preset target parameter values ​​to verify whether the expected adjustment effect has been achieved. For example, if the actual value of the needle length after adjustment is consistent with the target value, then the adjustment can be considered effective. The collected multivariate data is analyzed using statistical tools to determine the interactions and mutual influences between the parameters, which helps to deeply understand the complex relationships in the production process. Then, the system uses advanced anomaly detection algorithms to conduct in-depth analysis of the real-time monitored data to identify anomalies in the production process. These anomalies may indicate potential failures or production problems. Once an anomaly is detected, the system will classify and record it. These classifications help with subsequent analysis and processing. Based on the data mining algorithm, the system conducts a preliminary analysis of the recorded anomalies to find possible causes and solutions. The analysis results will be compiled into a report for reference by managers. The system evaluates the effectiveness of the current adjustment strategy, including the effect, efficiency and possible risks of the adjustment. This evaluation is comprehensive, considering not only short-term effects but also long-term effects. Based on the evaluation results, the system will iteratively optimize the adjustment strategy. This may include adjusting the target value of the parameter, optimizing the adjustment algorithm, adding new adjustment steps, etc. The purpose of optimization is to improve production efficiency, reduce production costs and improve product quality. The iterative adjustment strategy is applied to actual production, and its effect is evaluated through real-time monitoring and data verification. This verification is continuous to ensure that the adjustment strategy is still effective in actual production. Regularly evaluate the overall health of the production system, including the operating status of the equipment, production efficiency, product quality, etc. This evaluation helps to detect potential problems in a timely manner and take corresponding preventive measures. Based on the evaluation results of the overall health of the production system, the system formulates preventive maintenance plans. These plans may include equipment maintenance, spare parts replacement, system upgrades, etc., with the aim of preventing potential problems from occurring. Perform the corresponding maintenance tasks according to the maintenance plan, and track and record the maintenance results. This helps to ensure the stable operation of the system and extend the service life of the equipment.

[0124] The effects of the above technical solutions are: real-time monitoring of the change data of key parameters can immediately discover problems in the production process, and verify the adjustment effect by comparing the target parameter values. This real-time feedback ensures timely adjustment and optimization of the production process. Verifying the adjustment effect helps to ensure that production activities are carried out according to the predetermined goals, improving the controllability and accuracy of production. Using statistical tools for multivariate analysis can deeply understand the interaction and mutual influence between various parameters, and provide a scientific basis for production optimization. Advanced anomaly detection algorithms can accurately identify abnormal points in the production process, help to discover and solve problems in advance, and reduce production failures and downtime. Classifying and recording detected abnormalities helps to systematically manage abnormal information and facilitate subsequent analysis and processing. Preliminary analysis of abnormal situations based on data mining algorithms can reveal the causes and laws behind the abnormalities and provide strong support for formulating solutions. The analysis results are organized into reports to help managers fully understand the production situation and make scientific decisions. Evaluating the effectiveness of the current adjustment strategy can timely discover deficiencies and problems in the strategy and provide direction for optimization. Iterative optimization of the adjustment strategy based on the evaluation results can continuously improve the effect and efficiency of the adjustment and reduce production costs. The optimization process includes adjusting parameter target values, optimizing adjustment algorithms, adding new adjustment steps, etc. These measures can ensure that production activities are more efficient and accurate. Regularly evaluating the overall health of the production system can timely identify potential problems and formulate preventive maintenance plans to reduce the risk of equipment failure. Preventive maintenance plans include equipment overhaul, spare parts replacement, system upgrades and other measures to ensure stable operation of equipment and extend its service life. Performing maintenance tasks and tracking and recording maintenance results can help ensure the quality and effectiveness of maintenance work and improve the reliability and stability of the production system.

[0125] One embodiment of the present invention, as Figure 2 As shown, a glue needle gun production parameter upgrade optimization system, the system comprises:

[0126] Real-time monitoring module: a parameter monitoring system is set up on the glue needle gun production line, and the parameter monitoring system is used to monitor the production parameter data in real time, the production parameters including needle length, needle speed and glue amount, and the monitoring data is transmitted to the central control system in real time;

[0127] Real-time analysis module: The central control system performs real-time analysis on the received monitoring data and identifies abnormal conditions in the production process, including needle length error, needle speed fluctuation, and uneven glue amount;

[0128] Automatic adjustment module: Generates corresponding adjustment instructions based on abnormal situations. According to the generated adjustment instructions, the automatic adjustment system automatically adjusts the production parameters of the glue needle gun; such as adjusting the needle length through the servo motor, adjusting the needle speed through the inverter, and adjusting the glue amount through the precision metering pump.

[0129] Verification and optimization module: After the automatic adjustment system executes the adjustment command, the adjusted production parameters are monitored again in real time through the parameter monitoring system, and the monitoring data is fed back to the central control system for verification and optimization. If the adjustment effect does not meet the expectations, the adjustment command is regenerated until the optimal production state is achieved.

[0130] The working principle of the above technical solution is as follows: a parameter monitoring system is set up on the glue needle gun production line, which is responsible for real-time monitoring of key production parameters such as needle length, needle speed and glue amount. These monitoring data are transmitted to the central control system in real time to provide a basis for subsequent data analysis and adjustment; after receiving the monitoring data, the central control system will immediately perform real-time analysis. By analyzing the fluctuations and trends of the data and comparing with historical data, the system can identify abnormal conditions in the production process, such as needle length errors, needle speed fluctuations and uneven glue amount; once an abnormal condition is identified, the central control system will generate corresponding adjustment instructions based on the preset adjustment algorithms and strategies. These instructions will be sent to the automatic adjustment system, and the system will adjust the needle length through the servo motor, the needle speed through the inverter, and the glue amount through the precision metering pump according to the instructions. This automatic adjustment ensures the accuracy and efficiency of the adjustment; after the automatic adjustment system executes the adjustment instructions, the parameter monitoring system will again monitor the adjusted production parameters in real time. The monitoring data is again transmitted to the central control system to verify the adjustment effect. If the adjustment effect does not meet expectations, the central control system will re-analyze the data, generate new adjustment instructions, and continue to make adjustments through the automatic adjustment system until the optimal production state is achieved.

[0131] The effects of the above technical solution are as follows: by real-time monitoring and adjustment of production parameters, abnormal situations in the production process can be discovered and resolved in a timely manner, production interruptions and downtime can be reduced, and production efficiency can be improved; by accurately controlling key production parameters such as needle length, needle speed and glue quantity, the consistency and stability of the product can be ensured, the defective rate can be reduced, and product quality and customer satisfaction can be improved; by optimizing production parameters, the waste of raw materials and energy consumption can be reduced, and production costs can be reduced. At the same time, reducing the defective rate also avoids the additional costs caused by product rework or returns; the automatic adjustment system can quickly adjust production parameters according to different production needs and product specifications, and improve production flexibility and adaptability; the technical solution combines advanced monitoring, analysis and automatic adjustment technologies to realize the intelligent and automated production process, reduce dependence on manual labor, and improve production efficiency and management convenience; real-time monitoring and recording of production parameter data provide data support for the traceability and analysis of the production process, which helps to discover and solve problems in a timely manner and improve the level of production management; through precise control of production parameters, production accidents and safety hazards caused by improper parameter settings or excessive fluctuations are avoided, and production safety is improved.

[0132] In one embodiment of the present invention, the real-time monitoring module includes:

[0133] Building modules: Building a multi-level parameter monitoring system in the glue needle gun production line, the multi-level parameter monitoring system includes various sensors and data processing modules, the various sensors include laser rangefinders, high-speed encoders and high-precision flow sensors;

[0134] Transmission module: Build a sensor network based on various sensors, collect production parameter data in real time through the sensor network, and transmit the collected production parameter data to the data processing module;

[0135] Classification module: The data processing module automatically identifies and classifies the received data of different sensor types through intelligent algorithms and machine learning technology, and numbers and groups the data according to the sensor type;

[0136] Preprocessing module: Based on the data pipeline, the numbered and classified data are preprocessed separately, and the preprocessed monitoring data are compressed separately through the compression algorithm; the data preprocessing includes data cleaning and outlier removal;

[0137] Adjustment module: The pre-processed monitoring data is transmitted to the central control system through a multi-channel transmission protocol; during the transmission process, the bandwidth allocation is dynamically adjusted according to the real-time network status and data volume through a dynamic bandwidth allocation strategy.

[0138] The working principle of the above technical solution is as follows: In the glue needle gun production line, in order to comprehensively and accurately monitor the production parameters, a multi-level parameter monitoring system is first constructed. This system consists of various sensors and data processing modules. The sensors are selected according to the type of parameters to be monitored, such as laser rangefinders for measuring needle length, high-speed encoders for monitoring needle speed, and high-precision flow sensors for measuring glue volume; then, a touch sensor network is constructed based on these sensors. This network is responsible for real-time collection of data from each sensor, that is, production parameter data. The collected data will be transmitted to the data processing module in real time; in the data processing module, the received sensor data is first automatically identified and classified by intelligent algorithms and machine learning techniques. This is because there are various types of sensors on the production line, and the types of data collected are also different, so classification processing is required for subsequent analysis. At the same time, the data is numbered and grouped according to the type of sensor, so that the sensor and parameter type to which each data belongs can be accurately identified in subsequent processing; the classified data will enter the data preprocessing stage. This stage includes data cleaning and outlier removal. Data cleaning is to remove noise and invalid information in the data to ensure the accuracy and reliability of the data. The purpose of outlier elimination is to exclude abnormal data caused by equipment failure, operational errors, etc., to avoid interference with subsequent analysis and adjustment. The preprocessed data will be compressed by a compression algorithm to reduce the amount of data transmission and storage space occupied; finally, the preprocessed monitoring data will be transmitted to the central control system through a multi-channel transmission protocol. In this process, a dynamic bandwidth allocation strategy is adopted. This is because the network conditions and data volume may change in real time. If the bandwidth allocation is fixed, it may cause delays or losses in data transmission. The dynamic bandwidth allocation strategy can dynamically adjust the bandwidth allocation of each channel according to the real-time network conditions and data volume to ensure the real-time and integrity of the data.

[0139] The effect of the above technical solution is: by building a multi-level parameter monitoring system, using various sensors including laser rangefinders, high-speed encoders and high-precision flow sensors, the key parameters of the glue needle gun production line, such as needle length, needle speed and glue volume, can be fully and accurately monitored. This ensures the accuracy and reliability of the data and provides a solid foundation for subsequent production optimization; using intelligent algorithms and machine learning technology, the data processing module can automatically identify and classify data from different sensors, and perform numbering and grouping processing. This not only improves the data processing efficiency, but also enables the system to intelligently adapt to data of different types and sources, enhancing the flexibility and scalability of the system; through data preprocessing, such as data cleaning and outlier removal, noise and invalid information can be removed to improve the quality of data. At the same time, using compression algorithms to compress the preprocessed monitoring data can reduce the amount of data transmission and storage space occupied, reduce data transmission costs, and improve the real-time and reliability of data transmission; using multi-channel transmission protocols, the preprocessed monitoring data can be transmitted to the central control system separately. At the same time, through the dynamic bandwidth allocation strategy, the bandwidth allocation can be dynamically adjusted according to the real-time network status and data volume to ensure the real-time and integrity of the data. This avoids delays or losses in data transmission and improves the stability and reliability of the entire system; by real-time monitoring of production parameters and automatic adjustment of production parameters, abnormal situations in the production process can be discovered and resolved in a timely manner, production interruptions and downtime can be reduced, and production efficiency can be improved. At the same time, precise control of production parameters can ensure product consistency and stability, reduce defective rates, improve product quality and customer satisfaction; by optimizing production parameters and reducing raw material waste, production costs can be reduced. At the same time, reducing defective rates also avoids additional costs caused by product rework or returns; the entire system realizes the intelligence and automation of the production process, reduces dependence on manual labor, and improves production efficiency and management convenience. In addition, through real-time data monitoring and analysis, data support can be provided for production decisions, achieving more accurate production control and optimization.

[0140] In one embodiment of the present invention, the real-time analysis module includes:

[0141] Decompression module: The central control system decompresses the received compressed monitoring data and stores the decompressed monitoring data in different storage spaces;

[0142] Abnormal identification module: extracts features from the data in the storage space, and classifies the monitoring data based on machine learning algorithms to identify different types of abnormal situations, such as needle length error, needle speed fluctuation, and uneven glue amount; and prioritizes the abnormal situations based on their severity and impact;

[0143] Root cause analysis module: Root cause analysis is performed on the identified abnormalities to explore possible causes of the abnormalities, such as mechanical failure, supply chain problems, or operational errors, etc., correlation detection is performed between various abnormalities, and the impact relationship between different production parameters is analyzed;

[0144] Simulation module: Generates intelligent control strategies based on abnormal conditions and root cause analysis results, and performs simulation tests on the intelligent control strategies.

[0145] The working principle of the above technical solution is as follows: when the central control system receives the compressed monitoring data sent from the data processing module, it will first perform a decompression operation to restore the original form of the data. The decompressed monitoring data will be stored in different storage spaces in the central control system for subsequent efficient data processing and analysis; after the data storage is completed, the central control system will perform feature extraction on the data in the storage space. Feature extraction is to extract representative information or patterns from the original data through algorithms, and these features can reflect the main characteristics and trends of the data. Then, based on the machine learning algorithm, the system will classify and identify these features to distinguish different types of abnormal situations, such as needle length error, needle speed fluctuation, and uneven glue amount. According to the severity of the abnormal situation and the potential impact on the production process, the system will also prioritize the abnormal situation to ensure that those problems with the greatest impact on production are dealt with first; after identifying the abnormal situation, the system will perform root cause analysis. Root cause analysis is the process of exploring the root cause of the abnormal situation, and possible causes include mechanical failure, supply chain problems, operational errors, etc. The system will perform correlation detection on various abnormal situations and analyze the influence relationship between different production parameters to find out the root cause that may cause the abnormal situation. This correlation analysis helps to better understand the problems in the production process and provides strong support for subsequent solution strategies; after the root cause analysis is completed, the central control system will generate intelligent control strategies based on the abnormal situation and the root cause analysis results. These strategies are solutions to specific abnormal situations, aiming to eliminate or mitigate the impact of abnormal situations by adjusting production parameters or taking other measures. In order to verify the effectiveness of the strategy, the system will conduct simulation tests on it. Simulation is to simulate the actual production process in a virtual environment and apply the generated intelligent control strategy to observe its effect. In this way, the system can predict the performance of the strategy in actual production and further optimize and adjust the strategy based on the test results.

[0146] The effects of the above technical solutions are as follows: the decompression process ensures the originality and integrity of the data, providing an accurate data basis for subsequent analysis. Feature extraction and classification recognition based on machine learning algorithms can efficiently and accurately identify different types of abnormalities, such as needle length error, needle speed fluctuation, and uneven glue amount, improving the accuracy and efficiency of fault detection. The processing priority is divided according to the severity and impact of the abnormal situation, ensuring that the problems with the greatest impact on production are dealt with first, so as to quickly restore the stability and efficiency of the production line. Root cause analysis of the identified abnormalities can deeply explore the root causes of the abnormalities, such as mechanical failures, supply chain problems, or operational errors, providing strong support for subsequent solution strategies. By associating detection and analyzing the influence relationship between different production parameters, a more comprehensive understanding of the problems in the production process can be achieved, which helps to formulate more effective solutions. Based on the abnormal situations and root cause analysis results, intelligent control strategies are generated, which can accurately control specific problems and help to quickly restore the normal operation of the production line. Simulation tests of intelligent control strategies can predict the effects of strategies without interfering with actual production, and further optimize and adjust them according to the test results to ensure that the best results can be achieved in actual applications. Through real-time monitoring, accurate identification, in-depth analysis and effective response to abnormal situations in the production process, the stability and reliability of the production line can be ensured, production interruptions and downtime can be reduced, and production efficiency can be improved. Precision control can ensure product consistency and stability, reduce defective rates, and improve product quality and customer satisfaction. Through rapid response and effective handling of abnormal situations, losses caused by production interruptions and downtime can be reduced, and production costs can be reduced. Precision control can reduce the waste of raw materials and energy consumption, further reducing production costs.

[0147] In one embodiment of the present invention, the automatic adjustment module includes:

[0148] Decoding module: The central control system receives the intelligent control strategy, decodes the received intelligent control strategy, and clarifies the target adjustment value of each production parameter (needle length, needle speed, glue amount);

[0149] Assessment module: Based on historical data and the current production environment, the risk of the target adjustment value is assessed; for example, a needle length that is too short may result in insufficient product strength, a needle speed that is too fast may cause increased equipment wear, and uneven glue volume may result in unstable product quality. Based on the assessment results, a risk level is assigned to each risk and its likelihood of occurrence is predicted;

[0150] Instruction generation module: Generates specific adjustment instructions based on the target adjustment value and risk assessment results; these instructions include the rotation angle of the servo motor, the frequency setting of the inverter, the flow control of the precision metering pump, etc. The generated adjustment instructions are prioritized based on multiple factors, including the urgency of the adjustment, the impact on overall production, and the cost-effectiveness of the adjustment; for example, if the needle length error is too large and affects product quality, the priority of the instruction will be relatively high.

[0151] Comparison and verification module: Before executing the adjustment instruction, the parameter monitoring system collects data from the production line in real time and compares it with the target value. If there is a large difference between the real-time data and the target value, an alarm is triggered; and the adjustment instruction is fine-tuned based on the verification result of the real-time data; for example, if the real-time data shows that the needle speed is close to the target value, the system may reduce the adjustment range of the inverter.

[0152] Operation execution module: Send adjustment instructions to the automatic adjustment system and execute adjustment operations based on a multi-stage adjustment strategy. The multi-stage adjustment strategy includes that after each stage, the system will evaluate the adjustment effect and decide whether to continue to the next stage or end the adjustment. During the adjustment process, feedback data is collected in real time, including adjustment speed, adjustment accuracy, product quality changes, etc. Feedback data is learned through machine learning algorithms to continuously optimize adjustment strategies and algorithms. For example, the system may adjust the risk assessment model or priority sorting algorithm based on feedback data.

[0153] The working principle of the above technical solution is as follows: after receiving the intelligent control strategies, the central control system first decodes these strategies to clarify the target adjustment values ​​of each production parameter (needle length, needle speed, glue amount). These target adjustment values ​​are set by the system to optimize the production process based on the previous abnormal detection and analysis results; next, the system evaluates the risks that may be caused by the target adjustment values ​​based on historical data and the current production environment. This evaluation is comprehensive and detailed, taking into account various possible factors, such as insufficient product strength due to too short needle length, increased equipment wear due to too fast needle speed, and product quality problems due to uneven glue amount. Based on these evaluations, the system assigns a risk level to each potential risk and predicts its possibility of occurrence; after clarifying the target adjustment value and evaluating the potential risks, the system begins to generate specific adjustment instructions. These instructions are for each control element on the production line, such as the rotation angle of the servo motor, the frequency setting of the inverter, the flow control of the precision metering pump, etc. At the same time, the system will prioritize the generated adjustment instructions based on multiple factors (such as the urgency of the adjustment, the impact on the overall production, and the cost-effectiveness of the adjustment). For example, if the needle length error is too large and has a serious impact on product quality, the priority of the instruction to adjust the needle length will be relatively high; before executing the adjustment instruction, the system will collect data on the production line in real time through the parameter monitoring system and compare and verify it with the previously set target value. If there is a large difference between the real-time data and the target value, the system will trigger an alarm and fine-tune the adjustment instruction based on the verification results of the real-time data. This fine-tuning is dynamic and flexible, which ensures the accuracy and effectiveness of the adjustment process; after the fine-tuning is completed, the system sends the adjustment instruction to the automatic adjustment system and starts to implement a multi-stage adjustment strategy. This strategy is implemented step by step. After each stage, the system will evaluate the adjustment effect and decide whether to continue to the next stage or end the adjustment. During the entire adjustment process, the system will collect feedback data in real time, including adjustment speed, adjustment accuracy, and product quality changes. The system learns this data through machine learning algorithms and continuously optimizes the risk assessment model, priority sorting algorithm, and the adjustment strategy itself.

[0154] The effect of the above technical solution is: by decoding the intelligent control strategy and clarifying the target adjustment value of each production parameter, the system can ensure the accuracy of the adjustment. This helps to optimize the production process and improve product quality. According to the comparison, verification and fine-tuning of real-time data, the system can flexibly adapt to changes in the production environment to ensure that the adjustment operation is more in line with actual production needs. Based on historical data and the current production environment, the target adjustment value is risk assessed, and the system can predict potential risks and assign risk levels to them, so as to take preventive measures in advance to reduce production risks. This risk assessment helps enterprises better understand potential problems in the production process and formulate corresponding response strategies. According to multiple factors (such as the urgency of the adjustment, the impact on the overall production, and the cost-effectiveness of the adjustment), the adjustment instructions are prioritized. The system can ensure that the problems that have the greatest impact on production are dealt with first, thereby improving production efficiency and response speed. This priority sorting helps enterprises to achieve optimal allocation of resources and ensure the smooth progress of the production process. During the adjustment process, feedback data is collected in real time, including adjustment speed, adjustment accuracy, product quality changes, etc. The system can timely understand the adjustment effect and make optimization adjustments based on the feedback data. By learning the feedback data through machine learning algorithms, the system can continuously optimize the adjustment strategy and algorithm to improve the adjustment accuracy and efficiency. Through precise adjustment, risk assessment and prevention, and real-time feedback and optimization, the system can ensure the stability and reliability of the production process and reduce the probability of production interruptions and failures. This helps to improve production efficiency and product quality, reduce production costs, and enhance the market competitiveness of enterprises. This technical solution not only realizes the intelligent regulation of the production process, but also has the ability of autonomous learning and optimization. The system can continuously optimize and adjust strategies and algorithms based on real-time data and feedback data to improve the accuracy and efficiency of intelligent decision-making. This intelligent decision-making and autonomous learning ability can help enterprises better respond to market changes and competitive challenges and achieve sustainable development.

[0155] In one embodiment of the present invention, the verification optimization module includes:

[0156] Change data collection module: During the production process, multiple key parameters (such as needle length, needle speed, glue amount, etc.) are monitored in real time and their change data are collected. By comparing the target parameter value with the actual monitored parameter value, it is verified whether the expected adjustment effect has been achieved;

[0157] Variable analysis module: Use statistical tools to conduct multivariate analysis on the collected data, determine the interaction and mutual influence between various parameters, and use advanced anomaly detection algorithms to conduct in-depth analysis of real-time monitoring data to identify abnormal points in the production process;

[0158] Classification and recording module: classifies and records detected anomalies, performs preliminary analysis on recorded anomalies based on data mining algorithms, and compiles the mined analysis results into reports;

[0159] Iterative Optimization Module: Evaluate the effectiveness of the current adjustment strategy, including the effect, efficiency and possible risks of the adjustment. According to the evaluation results, iteratively optimize the adjustment strategy, such as adjusting the target value of the parameter, optimizing the adjustment algorithm, adding new adjustment steps, etc.

[0160] Application module: Apply the iterative adjustment strategy to actual production, evaluate its effect through real-time monitoring and data verification, and regularly evaluate the overall health of the production system, including equipment operation status, production efficiency, product quality and other aspects.

[0161] Maintenance module: According to the evaluation results, formulate preventive maintenance plans, including equipment maintenance, spare parts replacement, system upgrades, etc., to prevent potential problems from occurring, perform corresponding maintenance tasks according to the maintenance plan, and track and record maintenance results to ensure the stable operation of the system.

[0162] The working principle of the above technical solution is as follows: during the production process, the system monitors multiple key parameters (such as needle length, needle speed, glue amount, etc.) in real time and collects the change data of these parameters through sensors and other devices. The real-time collected data will be compared with the preset target parameter values ​​to verify whether the expected adjustment effect has been achieved. For example, if the actual value of the needle length after adjustment is consistent with the target value, then the adjustment can be considered effective. The collected multivariate data is analyzed using statistical tools to determine the interactions and mutual influences between the parameters, which helps to deeply understand the complex relationships in the production process. Then, the system uses advanced anomaly detection algorithms to conduct in-depth analysis of the real-time monitored data to identify anomalies in the production process. These anomalies may indicate potential failures or production problems. Once an anomaly is detected, the system will classify and record it. These classifications help with subsequent analysis and processing. Based on the data mining algorithm, the system conducts a preliminary analysis of the recorded anomalies to find possible causes and solutions. The analysis results will be compiled into a report for reference by managers. The system evaluates the effectiveness of the current adjustment strategy, including the effect, efficiency and possible risks of the adjustment. This evaluation is comprehensive, considering not only short-term effects but also long-term effects. Based on the evaluation results, the system will iteratively optimize the adjustment strategy. This may include adjusting the target value of the parameter, optimizing the adjustment algorithm, adding new adjustment steps, etc. The purpose of optimization is to improve production efficiency, reduce production costs and improve product quality. The iterative adjustment strategy is applied to actual production, and its effect is evaluated through real-time monitoring and data verification. This verification is continuous to ensure that the adjustment strategy is still effective in actual production. Regularly evaluate the overall health of the production system, including the operating status of the equipment, production efficiency, product quality, etc. This evaluation helps to detect potential problems in a timely manner and take corresponding preventive measures. Based on the evaluation results of the overall health of the production system, the system formulates preventive maintenance plans. These plans may include equipment maintenance, spare parts replacement, system upgrades, etc., with the aim of preventing potential problems from occurring. Perform the corresponding maintenance tasks according to the maintenance plan, and track and record the maintenance results. This helps to ensure the stable operation of the system and extend the service life of the equipment.

[0163] The effects of the above technical solutions are: real-time monitoring of the change data of key parameters can immediately discover problems in the production process, and verify the adjustment effect by comparing the target parameter values. This real-time feedback ensures timely adjustment and optimization of the production process. Verifying the adjustment effect helps to ensure that production activities are carried out according to the predetermined goals, improving the controllability and accuracy of production. Using statistical tools for multivariate analysis can deeply understand the interaction and mutual influence between various parameters, and provide a scientific basis for production optimization. Advanced anomaly detection algorithms can accurately identify abnormal points in the production process, help to discover and solve problems in advance, and reduce production failures and downtime. Classifying and recording detected abnormalities helps to systematically manage abnormal information and facilitate subsequent analysis and processing. Preliminary analysis of abnormal situations based on data mining algorithms can reveal the causes and laws behind the abnormalities and provide strong support for formulating solutions. The analysis results are organized into reports to help managers fully understand the production situation and make scientific decisions. Evaluating the effectiveness of the current adjustment strategy can timely discover deficiencies and problems in the strategy and provide direction for optimization. Iterative optimization of the adjustment strategy based on the evaluation results can continuously improve the effect and efficiency of the adjustment and reduce production costs. The optimization process includes adjusting parameter target values, optimizing adjustment algorithms, adding new adjustment steps, etc. These measures can ensure that production activities are more efficient and accurate. Regularly evaluating the overall health of the production system can timely identify potential problems and formulate preventive maintenance plans to reduce the risk of equipment failure. Preventive maintenance plans include equipment overhaul, spare parts replacement, system upgrades and other measures to ensure stable operation of equipment and extend its service life. Performing maintenance tasks and tracking and recording maintenance results can help ensure the quality and effectiveness of maintenance work and improve the reliability and stability of the production system.

[0164] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for upgrading and optimizing production parameters of a glue needle gun, characterized in that: The method comprises: S1. A parameter monitoring system is set up on the glue needle gun production line, and the production parameter data is monitored in real time through the parameter monitoring system, and the monitoring data is transmitted to the central control system in real time; S2. The central control system performs real-time analysis on the received monitoring data and identifies abnormal conditions in the production process; S3, generating corresponding adjustment instructions based on the abnormal situation, and automatically adjusting the production parameters of the glue needle gun through the automatic adjustment system according to the generated adjustment instructions; S4. After the automatic adjustment system executes the adjustment command, the adjusted production parameters are again monitored in real time through the parameter monitoring system, and the monitoring data is fed back to the central control system for verification and optimization; The S3 includes: S31. The central control system receives the intelligent control strategy, decodes the received intelligent control strategy, and clarifies the target adjustment value of each production parameter; S32. Evaluate the risk of the target adjustment value based on historical data and the current production environment; assign a risk level to each risk based on the evaluation results, and predict the probability of the risk occurring; S33. Generate specific adjustment instructions based on the target adjustment value and the risk assessment result; prioritize the generated adjustment instructions based on multiple factors; S34. Before executing the adjustment instruction, the data of the production line is collected in real time through the parameter monitoring system, and compared with the target value for verification. If there is a difference between the real-time data and the target value, an alarm is triggered; and the adjustment instruction is fine-tuned according to the verification result of the real-time data; S35. Send the adjustment instruction to the automatic adjustment system, and execute the adjustment operation based on the multi-stage adjustment strategy. During the adjustment process, feedback data is collected in real time, and the feedback data is learned through the machine learning algorithm to continuously optimize the adjustment strategy and algorithm.

2. According to claim 1, a method for upgrading and optimizing production parameters of a glue needle gun is characterized in that: Said S1 comprises: S11. Construct a multi-level parameter monitoring system in the glue needle gun production line, wherein the multi-level parameter monitoring system includes various sensors and a data processing module; S12. Building a sensor network based on various sensors, collecting production parameter data in real time through the sensor network, and transmitting the collected production parameter data to a data processing module; S13, the data processing module automatically identifies and classifies the received data of different sensor types through intelligent algorithms and machine learning technology, and numbers and groups the data according to the sensor type; S14, based on the data pipeline, pre-processing the numbered and classified data respectively, and compressing the pre-processed monitoring data respectively through a compression algorithm; S15. The pre-processed monitoring data are transmitted to the central control system through a multi-channel transmission protocol. During the transmission process, the bandwidth allocation is dynamically adjusted according to the real-time network status and data volume through a dynamic bandwidth allocation strategy.

3. According to claim 1, a method for upgrading and optimizing production parameters of a glue needle gun is characterized in that: The S2 comprises: S21, the central control system decompresses the received compressed monitoring data respectively, and stores the decompressed monitoring data in different storage spaces respectively; S22, extracting features from the data in the storage space, and classifying the monitoring data based on a machine learning algorithm to identify different types of abnormal situations, and assigning a processing priority to the abnormal situations based on the severity and impact of the abnormal situations; S23. Analyze the root causes of the identified abnormalities, detect the associations between various abnormalities, and analyze the influence relationships between different production parameters; S24. Generate an intelligent control strategy based on the abnormal situation and root cause analysis results, and perform simulation tests on the intelligent control strategy.

4. According to claim 1, a method for upgrading and optimizing production parameters of a glue needle gun is characterized in that: The S4 comprises: S41. During the production process, multiple key parameters are monitored in real time, and the change data of key parameters are collected. By comparing the target parameter values ​​with the actually monitored parameter values, it is verified whether the expected adjustment effect has been achieved; S42. Use statistical tools to conduct multivariate analysis on the collected data to determine the interactions and mutual influences between the parameters. Use advanced anomaly detection algorithms to conduct in-depth analysis of real-time monitoring data to identify anomalies in the production process. S43, classifying and recording the detected abnormalities, performing preliminary analysis on the recorded abnormalities based on a data mining algorithm, and compiling the mined analysis results into a report; S44, evaluating the effect of the current adjustment strategy, and iteratively optimizing the adjustment strategy according to the evaluation result; S45. Apply the iterated adjustment strategy to actual production, evaluate the effect of the adjustment strategy through real-time monitoring and data verification, and regularly evaluate the overall health of the production system; S46. Develop a preventive maintenance plan based on the evaluation results, perform corresponding maintenance tasks according to the maintenance plan, and track and record the maintenance results.

5. A glue needle gun production parameter upgrade and optimization system, characterized in that: The system comprises: Real-time monitoring module: a parameter monitoring system is set up on the glue needle gun production line, the parameter monitoring system is used to monitor the production parameter data in real time, and the monitoring data is transmitted to the central control system in real time; Real-time analysis module: The central control system performs real-time analysis on the received monitoring data and identifies abnormal situations in the production process. Automatic adjustment module: Generates corresponding adjustment instructions based on abnormal situations. According to the generated adjustment instructions, the automatic adjustment system automatically adjusts the production parameters of the glue needle gun; Verification and optimization module: After the automatic adjustment system executes the adjustment command, the adjusted production parameters are again monitored in real time through the parameter monitoring system, and the monitoring data is fed back to the central control system for verification and optimization; The automatic adjustment module comprises: Decoding module: The central control system receives the intelligent control strategy, decodes the received intelligent control strategy, and clarifies the target adjustment value of each production parameter; Assessment module: Based on historical data and the current production environment, the risk of the target adjustment value is assessed; based on the assessment results, a risk level is assigned to each risk and the probability of risk occurrence is predicted; Instruction generation module: Generates specific adjustment instructions based on target adjustment values ​​and risk assessment results; prioritizes the generated adjustment instructions based on multiple factors; Comparison and verification module: Before executing the adjustment instruction, the parameter monitoring system collects the data of the production line in real time and compares and verifies it with the target value. If there is a difference between the real-time data and the target value, an alarm is triggered; and the adjustment instruction is fine-tuned according to the verification result of the real-time data; Operation execution module: sends adjustment instructions to the automatic adjustment system and executes adjustment operations based on multi-stage adjustment strategies. During the adjustment process, feedback data is collected in real time, and the feedback data is learned through machine learning algorithms to continuously optimize adjustment strategies and algorithms.

6. According to claim 5, a glue needle gun production parameter upgrade and optimization system is characterized in that: The real-time monitoring module comprises: Building modules: Building a multi-level parameter monitoring system in the glue needle gun production line, the multi-level parameter monitoring system includes various sensors and data processing modules; Transmission module: Build a sensor network based on various sensors, collect production parameter data in real time through the sensor network, and transmit the collected production parameter data to the data processing module; Classification module: The data processing module automatically identifies and classifies the received data of different sensor types through intelligent algorithms and machine learning technology, and numbers and groups the data according to the sensor type; Preprocessing module: Based on the data pipeline, the numbered and classified data are preprocessed separately, and the preprocessed monitoring data are compressed separately through the compression algorithm; Adjustment module: The pre-processed monitoring data is transmitted to the central control system through a multi-channel transmission protocol; during the transmission process, the bandwidth allocation is dynamically adjusted according to the real-time network status and data volume through a dynamic bandwidth allocation strategy.

7. According to claim 5, a glue needle gun production parameter upgrade and optimization system is characterized in that: The real-time analysis module comprises: Decompression module: The central control system decompresses the received compressed monitoring data and stores the decompressed monitoring data in different storage spaces; Abnormal identification module: extracts features from the data in the storage space, and classifies the monitoring data based on machine learning algorithms to identify different types of abnormal situations, and prioritizes the abnormal situations based on their severity and impact; Root cause analysis module: Root cause analysis of identified abnormalities, correlation detection between various abnormalities, and analysis of the impact relationship between different production parameters; Simulation module: Generates intelligent control strategies based on abnormal conditions and root cause analysis results, and performs simulation tests on the intelligent control strategies.

8. According to claim 5, a glue needle gun production parameter upgrade and optimization system is characterized in that: The verification optimization module comprises: Change data collection module: During the production process, multiple key parameters are monitored in real time, and the change data of key parameters are collected. By comparing the target parameter value with the actual monitored parameter value, it is verified whether the expected adjustment effect has been achieved; Variable analysis module: Use statistical tools to conduct multivariate analysis on the collected data, determine the interaction and mutual influence between various parameters, and use advanced anomaly detection algorithms to conduct in-depth analysis of real-time monitoring data to identify abnormal points in the production process; Classification and recording module: classifies and records detected anomalies, performs preliminary analysis on recorded anomalies based on data mining algorithms, and compiles the mined analysis results into reports; Iterative optimization module: evaluates the effectiveness of the current adjustment strategy and iteratively optimizes the adjustment strategy based on the evaluation results; Application module: Apply the iterated adjustment strategy to actual production, evaluate the adjustment effect through real-time monitoring and data verification, and regularly evaluate the overall health of the production system; Maintenance module: formulate preventive maintenance plan based on the evaluation results, perform corresponding maintenance tasks according to the maintenance plan, and track and record the maintenance results.

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