An intelligent copper wire bonding equipment cluster control method and system based on an environmental internet of things

Abstract

This invention provides a method and system for intelligent cluster control of copper bonding wire equipment based on the Internet of Things (IoT) of the environment, relating to the field of copper bonding wire production control technology. The method includes acquiring historical production control data of the cluster and extracting monitoring features based on monitoring categories to form cluster production control parameter feature data; collecting real-time production control data of the cluster and combining it with the cluster production control feature data to perform production status analysis and form real-time production status monitoring result data. This method utilizes the IoT of the environment to construct a monitoring system for the control of the entire copper bonding wire production cluster, thereby providing a data foundation for intelligent regulation to fully meet the high-precision and high-quality production control requirements of copper bonding wire.

Description

Technical Field

[0001] This invention relates to the field of copper bonding wire production control technology, and more specifically, to a method and system for intelligent copper bonding wire equipment cluster control based on the Internet of Things (IoT) of the environment. Background Technology

[0002] Copper bonding wire is a key material in semiconductor packaging. High-quality copper bonding wire effectively ensures packaging quality, thus requiring precise and efficient production control. Currently, with advancements in science and technology, the Internet of Things (IoT) is becoming increasingly widespread. Especially compared to environmental IoT, which relies on ambient energy for operation and offers low-power, low-consumption signal transmission, it plays a more efficient and convenient role in production control and monitoring.

[0003] However, current environmental IoT systems lack sufficient systematization for production control and monitoring, and cannot integrate data resources for reasonable real-time processing of production control. Especially for production processes like bonding copper wire, which have high precision and high quality requirements, it is even more necessary to rationally coordinate and efficiently control the entire production cluster equipment to effectively ensure the quality of the bonded copper wire.

[0004] Therefore, designing a cluster control method and system for intelligent bonding copper wire equipment based on the Internet of Things (IoT) of the environment, and using the IoT of the environment to build a monitoring system for the control of the entire bonding copper wire production cluster, thereby providing an intelligent control data foundation for fully meeting the high-precision and high-quality production control requirements of bonding copper wire, is an urgent problem to be solved. Summary of the Invention

[0005] The purpose of this invention is to provide a smart copper wire bonding equipment cluster control method based on the Internet of Things (IoT) of the environment. By utilizing the IoT to construct a production monitoring and control system for the copper wire bonding equipment cluster, this method performs feature analysis based on historical big data on the control parameters to be monitored, determining the reasonable range of these parameters during the production process. It also provides accurate and reasonable parameter comparison features for subsequent real-time control monitoring and analysis, ensuring accurate and effective real-time monitoring and control of the control parameters. This effectively guarantees the production quality of the bonded copper wire. Furthermore, by employing the IoT for data collection and analysis, and leveraging the energy of the surrounding environment as a primary energy source, the energy generated by the equipment cluster during production can be further rationally utilized, adding a new element to the concept of a smart factory and further improving energy utilization efficiency, resulting in energy conservation and environmental protection. Moreover, because the IoT utilizes low-power information transmission, it can effectively transmit and disseminate information in complex equipment cluster areas without significant signal attenuation due to terrain or equipment installation conditions.

[0006] The present invention also aims to provide an intelligent cluster control system for copper bonding equipment based on the Internet of Things (IoT) of the environment. This system utilizes environmental IoT-type data acquisition terminals to collect data from the surrounding environment, fully leveraging the diffused energy generated during copper bonding production and improving energy efficiency. Data collected at each location point is uploaded via low-power transmission, ensuring stability and reliability and avoiding signal degradation caused by obstructions. A database stores historical data appropriately, providing a foundation for big data analysis. The processing and feedback units promptly extract feature data and perform real-time data comparison and analysis to identify anomalies, enabling effective and accurate control of cluster production and ensuring the quality of the bonded copper wire. The entire system tightly integrates data acquisition, processing, and analysis to form a complete production control and monitoring system, providing an intelligent data foundation for meeting the high-precision and high-quality production control requirements of copper bonding.

[0007] In a first aspect, the present invention provides a cluster control method for intelligent bonding copper wire equipment based on the Internet of Things in the environment, including acquiring historical data of cluster production control and extracting monitoring features based on monitoring categories to form cluster production control parameter feature data; collecting real-time data of cluster production control and combining the cluster production control feature data to perform production status analysis to form real-time monitoring result data of production status.

[0008] In this invention, the method utilizes the Internet of Things (IoT) in the environment to construct a production monitoring and control system for a cluster of copper bonding equipment. On one hand, it performs feature analysis based on historical big data on the control parameters to be monitored, determining the reasonable range of these parameters during the production process. On the other hand, it provides accurate and reasonable parameter comparison features for subsequent real-time control monitoring and analysis, ensuring accurate and effective real-time monitoring and control of the control parameters. This effectively guarantees the production quality of the bonded copper wire. Furthermore, by employing the IoT for data collection and analysis, and leveraging the energy of the surrounding environment as a primary energy source, the method further optimizes the energy generated by the equipment cluster during production, adding a new element to the concept of a smart factory and further improving energy utilization efficiency, resulting in energy conservation and environmental protection. Moreover, because the IoT utilizes low-power information transmission, it can effectively transmit and disseminate information in complex equipment cluster areas without significant signal attenuation due to terrain or equipment installation conditions.

[0009] One possible implementation involves acquiring historical cluster production control data and extracting monitoring features based on monitoring categories to form cluster production control parameter feature data. This includes: acquiring historical cluster production control data and extracting features based on adjustment control parameters to form regulation parameter feature data; and acquiring historical cluster production control data and extracting features based on capacity control parameters to form capacity control parameter feature data.

[0010] In this invention, feature information of control parameters in the production process of bonding copper wire is extracted. Two types of control parameters are mainly considered: one type is the control parameter that determines the quality of the bonding copper wire in the production process, and the other type is the control parameter that affects the production capacity of the bonding copper wire. By extracting features from these two types of control parameters, it is possible to achieve in-process quality control during the production of bonding copper wire, so as to ensure the quality of the bonding copper wire by adjusting the parameters in real time. It is also possible to achieve control and monitoring of output, so as to avoid large fluctuations in output caused by changes in control parameters, which may result in failure to meet production demand.

[0011] One possible implementation involves acquiring historical production control data from the cluster, performing feature extraction based on adjustment control parameters, and forming regulation parameter feature data. This includes: using the performance parameters of the bonding copper wire as a reference, extracting historical regulation parameters for all production projects with the same bonding copper wire performance parameters from the historical production control data of the cluster; for different adjustment control parameters, performing the following data type-based feature extraction processing based on the corresponding historical regulation parameters to form corresponding regulation parameter features: for continuous data collected from adjustment control parameters, determining the minimum and maximum values ​​based on the corresponding historical regulation parameters to form continuous regulation parameter features; for discrete data collected from adjustment control parameters, determining the minimum, maximum, minimum acquisition time interval, and maximum acquisition time interval based on the corresponding historical regulation parameters to form discrete regulation parameter features; and combining the regulation parameter features of all adjustment control parameters to form a regulation parameter feature set, and then calibrating the bonding copper wire performance parameters.

[0012] In this invention, the feature information extraction of adjustment control parameters that determine the production quality of bonded copper wire takes into account the types of parameter data collected for different adjustment control parameters. Different types of data have different aspects and focuses for feature extraction, ensuring an accurate and reasonable reflection of the characteristics of the control parameters. The parameter data acquired for the adjustment control parameters in the bonded copper wire production process mostly fall into two categories: continuous data types and discrete data types. For continuous data types, the main focus is on obtaining the maximum and minimum values ​​that can be determined from historical data, thereby establishing a reasonable range. Of course, for historical large datasets, deviations in the maximum and minimum values ​​can also be considered. Therefore, preprocessing can be performed before determining the maximum and minimum values ​​to eliminate the impact of a few data deviations on value extraction. For discrete data types, although there is also a certain reasonable numerical range, the time characteristics of discrete data acquisition are also considered. Therefore, the time interval between adjacent data acquisitions is also statistically analyzed for maximum and minimum values ​​to form an effective and reasonable acquisition time interval feature. Similarly, for extracting extreme values, preprocessing the initial large dataset to eliminate deviations is also necessary. In addition, it should be noted that, considering the application scenarios and other factors, the performance parameters of the bonding copper wire may differ each time it is produced. Therefore, the range of adjustment and control parameters should be analyzed and processed using big data from the same bonding copper wire production project to avoid significant deviations in the obtained characteristics due to differences in the performance parameters of the bonding copper wire.

[0013] One possible implementation involves acquiring historical production control data for the cluster and extracting features based on capacity control parameters to form capacity control parameter feature data. This includes: using the performance parameters of the bonding copper wire as a reference, extracting historical capacity parameters for all capacity control parameters in production projects with the same bonding copper wire performance parameters from the historical production control data of the cluster; arranging different capacity control parameters sequentially according to production processes to form a production control parameter sequence set; determining the effective capacity fluctuation range of each capacity control parameter within the capacity control parameter sequence set for different production projects with the same bonding copper wire performance parameters, forming the process capacity project relationship feature corresponding to the capacity control parameter sequence set; and performing a union operation on the effective capacity fluctuation ranges of the same capacity control parameter in the process capacity project relationship feature for all production projects with the same bonding copper wire performance parameters to form the process capacity relationship feature corresponding to the bonding copper wire performance parameters.

[0014] In this invention, for capacity control parameters, capacity is affected by both the process steps and the changes in control parameters. Therefore, when extracting features, the performance parameters of the bonding copper wire are used as the classification method to determine more reasonable feature data. Of course, for capacity control parameters, since the final output of the bonding copper wire is gradually formed from the raw materials through sequentially arranged processes, different processes will more or less damage some raw materials. Therefore, when obtaining features for capacity control parameters, the entire process flow is considered. That is, the parameters of capacity control parameters are expressed by forming a capacity chain based on the sequence of processes that transfer capacity.

[0015] One possible implementation involves collecting real-time production control data from the cluster and combining it with cluster production control characteristic data to perform production status analysis and generate real-time production status monitoring results. This includes: collecting real-time control parameters for adjustment control parameters and combining them with control parameter characteristic data to perform control production status analysis and generate control production status real-time monitoring results; and collecting real-time capacity control parameters for capacity control parameters and combining them with capacity control parameter characteristic data to perform capacity production status analysis and generate capacity production status real-time monitoring results.

[0016] In this invention, the collected real-time monitoring data is compared with large datasets to determine whether it represents reasonable parameters for control. Therefore, comparing real-time data with corresponding feature data allows for accurate, efficient, and reasonable real-time monitoring, ensuring effective and timely production control monitoring of cluster equipment.

[0017] One possible implementation involves collecting real-time control parameters of adjustment and control types, and combining this with characteristic data of the control parameters to analyze the control production status and generate real-time monitoring results. This includes: collecting real-time control parameters of adjustment and control types; and performing the following real-time self-check analysis on two real-time control parameters collected for the same adjustment and control type: if both real-time control parameters are empty, and other adjustment and control types have collected valid data, a control acquisition fault warning is generated; if both real-time control parameters are empty, and other adjustment and control types have not collected valid data, a non-working status is generated; if either of the two real-time control parameters is empty, and the other real-time control parameter is empty... If the corresponding control parameter features in the corresponding control parameter feature set are used to form a single effective control information, the real-time control parameter belonging to the control parameter feature corresponding to the corresponding control parameter feature set will be output. If either of the two real-time control parameters is a null parameter and the other real-time control parameter does not belong to the control parameter feature corresponding to the corresponding control parameter feature set, a dual control acquisition fault warning information will be formed. If the difference between the two real-time control parameters exceeds the comparison error threshold, a dual control acquisition error exceeding the limit warning information will be formed. If the difference between the two real-time control parameters does not exceed the comparison error threshold and both belong to the control parameter feature corresponding to the corresponding control parameter feature set, a dual control acquisition normal information will be formed, and the average real-time control parameter will be output.

[0018] In this invention, the real-time data comparison for adjusting control parameters differs from other data acquisition systems. Based on data collected by the Environmental Internet of Things (IoT), this application considers both the rationality of real-time data acquisition and the fact that the Environmental IoT, while utilizing environmental energy for data acquisition, saves energy and reduces data acquisition costs. The validity and correctness of the acquired data can be verified by collecting the same control parameter from two acquisition points. Compared to traditional data acquisition systems, this does not significantly increase costs, but provides a more effective analytical reference for judging the correctness and rationality of real-time data acquisition. It should be noted that the comparative analysis will differ depending on the conditions of the two real-time data points collected. When both real-time parameters are empty, the acquisition status of other control parameters is considered. If only the analyzed parameter is empty, it indicates an error in data extraction. If all control parameters are empty, it can be determined that the current cluster equipment has not started production. For two collected real-time parameters, if one has a parameter and the other does not, it is necessary to compare and analyze the acquired parameters using features extracted from big data. If the parameter falls within the feature range, the one collection point without extracted parameters is determined to be faulty, while the other is valid. If the collected parameter does not fall within the feature range, it proves that both collection points have failed. If both collection points have numerical data for their real-time parameters, but the difference between the two parameters is too large, it also proves that the collection point is faulty. If the difference between the two parameters is small and both fall within the corresponding feature range, the average parameter value can be used to represent the real-time parameter of the corresponding parameter. By comparing the parameters collected from two collection points with their corresponding feature ranges, the correctness of the collected data can be further accurately determined. Furthermore, the collected data can be used to determine whether production is proceeding, expanding the scope of parameter analysis and making the analysis more comprehensive and accurate.

[0019] As one possible implementation, the real-time control parameter belongs to the corresponding control parameter feature set and must meet the following conditions: for the real-time control parameter as continuous data, the value of the real-time control parameter belongs to the numerical range of the corresponding continuous control parameter feature; for the real-time control parameter as discrete data, the real-time control parameter ensures that the value of the real-time control parameter belongs to the numerical range of the corresponding continuous control parameter feature, and the range of the acquisition time interval of the real-time control parameter belongs to the acquisition time interval range of the corresponding continuous control parameter feature.

[0020] In this invention, when determining whether real-time data acquired for the same control parameter belongs to the corresponding feature range, it is necessary to determine the data type. For continuous data, it is sufficient to determine that the acquired real-time parameter belongs to the range established by the extreme values. However, for discrete data, it is necessary not only to determine that the parameter values ​​are within the range established by the corresponding extreme values, but also to determine that the range of the acquisition time intervals belongs to the range established by the corresponding extreme values.

[0021] One possible implementation involves collecting real-time capacity control parameters of capacity control parameters and combining them with characteristic data of these parameters to analyze the production status and generate real-time monitoring results. This includes: determining the real-time capacity control range for each capacity control parameter based on the collected real-time capacity control parameters; arranging the real-time capacity control ranges of different capacity control parameters according to the process sequence to generate real-time process capacity relationship data; determining the process capacity relationship characteristics corresponding to the real-time process capacity relationship data based on the performance parameters of the bonding copper wire, and combining this with the real-time monitoring results to analyze the production status and generate real-time production status monitoring results.

[0022] In this invention, after obtaining the real-time capacity control parameters, it is necessary to track the changes in output for each step of the entire process. Therefore, the real-time capacity control parameters are also arranged according to the process sequence and compared with the corresponding bonding copper wire performance parameters to achieve a reasonable and accurate judgment.

[0023] As one possible implementation, the process capacity relationship characteristics corresponding to the real-time relationship data of different process capacities are determined based on the performance parameters of the bonding copper wire. This is combined with real-time monitoring results of production status to analyze the capacity status and generate real-time monitoring results data. This data includes: if the different real-time capacity control ranges in the process capacity relationship data all fall within the effective fluctuation range of the corresponding capacity control parameters in the process capacity relationship characteristic data, then normal capacity control information is generated; if the real-time capacity control ranges in the process capacity relationship data all fall within the effective fluctuation range of the corresponding capacity control parameters in the process capacity relationship characteristic data, and all real-time control parameters under the corresponding process are normal, then non-control-related capacity anomaly warning information is generated; if the real-time capacity control ranges in the process capacity relationship data all fall within the effective fluctuation range of the corresponding capacity control parameters in the process capacity relationship characteristic data, and the real-time control parameters under the corresponding process are abnormal, then control-related capacity anomaly warning information is generated.

[0024] In this invention, different comparison results between real-time capacity parameters and relational characteristics will lead to different capacity status judgments during analysis and judgment. If each real-time capacity parameter falls within the corresponding capacity characteristic range, the entire production process is stable and the output consistently meets demand. If a real-time capacity parameter does not fall within the corresponding capacity characteristic range, and the adjustment control parameters for the corresponding process are confirmed to not fall within the corresponding characteristic range, then the abnormal capacity impact may be caused by changes in non-adjustment control parameters such as material transfer. If a real-time capacity parameter does not fall within the corresponding capacity characteristic range, and the adjustment control parameters for the corresponding process are confirmed to not fall within the corresponding characteristic range, then the abnormal capacity may be caused by abnormal adjustment control parameters. For different types of abnormal judgments, targeted production control adjustments can be made, greatly improving the timeliness and accuracy of production control.

[0025] Secondly, this invention provides a cluster control system for intelligent bonding copper wire equipment based on the Internet of Things (IoT). The system includes: an A-IoT acquisition terminal, comprising A-IoT acquisition terminals of different energy acquisition types, installed on corresponding process equipment, acquiring different real-time control parameters and real-time production capacity control parameters corresponding to the process; for A-IoT acquisition terminals acquiring different real-time control parameters, the acquisition of each adjustment control parameter is completed by two identical A-IoT acquisition terminals; an A-IoT data acquisition unit, used to collect real-time parameters acquired by all A-IoT acquisition terminals; an A-IoT database, used to store historical data of cluster production control; and an A-IoT data processing and feedback unit, used to acquire historical data of cluster production control from the A-IoT database, perform feature extraction to form cluster production control parameter feature data, and extract real-time parameters acquired by the A-IoT data acquisition unit, combining the cluster production control parameter feature data to perform production status analysis, forming real-time production status monitoring result data and issuing early warnings.

[0026] In this invention, the system utilizes environmental IoT-type data acquisition terminals to collect data from the surrounding environment, fully leveraging the diffused energy generated during copper wire bonding production and improving energy efficiency. Data collected at each location is uploaded via low-power transmission, ensuring stability and reliability and avoiding signal degradation caused by obstructions. A database stores historical data appropriately, providing a foundation for big data analysis. The processing and feedback units promptly extract feature data and perform real-time data comparison and analysis to identify anomalies, enabling effective and accurate control of cluster production and ensuring the quality of the bonded copper wire. The entire system tightly integrates data acquisition, processing, and analysis to form a complete production control and monitoring system, providing an intelligent control data foundation to fully meet the high-precision, high-quality production control requirements of copper wire bonding.

[0027] The beneficial effects of the intelligent copper wire bonding equipment cluster control method and system based on environmental Internet of Things provided by this invention are as follows:

[0028] This method utilizes the Internet of Things (IoT) in the environment to construct a production monitoring and control system for a cluster of copper bonding equipment. On one hand, it performs feature analysis based on historical big data on the control parameters to be monitored, determining the reasonable range of these parameters during production. On the other hand, it provides accurate and reasonable parameter comparison features for subsequent real-time control monitoring and analysis, ensuring accurate and effective real-time monitoring and control of the control parameters. This effectively guarantees the production quality of the bonded copper wire. Furthermore, by employing the IoT for data collection and analysis, and leveraging the energy of the surrounding environment as a primary energy source, this method further optimizes the energy generated by the equipment cluster during production, adding a new element to the concept of a smart factory and further improving energy utilization efficiency, resulting in energy conservation and environmental protection. Moreover, because the IoT utilizes low-power information transmission, it can effectively transmit and disseminate information in complex equipment cluster areas without significant signal attenuation due to terrain or equipment installation conditions.

[0029] This system utilizes environmental IoT-type data acquisition terminals to collect data from the surrounding environment, fully leveraging the diffused energy generated during copper wire bonding production and improving energy efficiency. Data collected at each location is uploaded via low-power transmission, ensuring stability and reliability and avoiding signal degradation caused by obstructions. A database stores historical data appropriately, providing a foundation for big data analysis. The processing and feedback units promptly extract feature data and perform real-time data comparison and analysis to identify anomalies, enabling effective and accurate control of cluster production and ensuring the quality of the bonded copper wire. The entire system tightly integrates data acquisition, processing, and analysis to form a complete production control and monitoring system, providing an intelligent data foundation for meeting the high-precision, high-quality production control requirements of copper wire bonding. Attached Figure Description

[0030] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments of the present invention will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 A flowchart illustrating the steps of a cluster control method for intelligent bonding copper wire equipment based on the Internet of Things (IoT) provided in this embodiment of the invention. Detailed Implementation

[0032] The technical solutions of the present invention will now be described with reference to the accompanying drawings in the embodiments of the present invention.

[0033] Copper bonding wire is a key material in semiconductor packaging. High-quality copper bonding wire effectively ensures packaging quality, thus requiring precise and efficient production control. Currently, with advancements in science and technology, the Internet of Things (IoT) is becoming increasingly widespread. Especially compared to environmental IoT, which relies on ambient energy for operation and offers low-power, low-consumption signal transmission, it plays a more efficient and convenient role in production control and monitoring.

[0034] However, current environmental IoT systems lack sufficient systematization for production control and monitoring, and cannot integrate data resources for reasonable real-time processing of production control. Especially for production processes like bonding copper wire, which have high precision and high quality requirements, it is even more necessary to rationally coordinate and efficiently control the entire production cluster equipment to effectively ensure the quality of the bonded copper wire.

[0035] refer to Figure 1This invention provides a method for controlling a cluster of intelligent copper wire bonding equipment based on the Internet of Things (IoT) of the environment. This method utilizes the IoT to construct a production monitoring and control system for the copper wire bonding equipment cluster. On one hand, it performs feature analysis based on historical big data on the control parameters to be monitored, determining the reasonable range of these parameters during production. On the other hand, it provides accurate and reasonable parameter comparison features for subsequent real-time control monitoring and analysis, ensuring accurate and effective real-time monitoring and control of the control parameters. This effectively guarantees the production quality of the bonded copper wire. Furthermore, by employing the IoT for data collection and analysis, and leveraging the energy of the surrounding environment as a primary energy source, the energy generated by the equipment cluster during production can be further rationally utilized, adding a new element to the concept of a smart factory and further improving energy utilization efficiency, resulting in energy conservation and environmental protection. Moreover, because the IoT utilizes low-power information transmission, it can effectively transmit and disseminate information in complex equipment cluster areas without significant signal attenuation due to terrain or equipment installation conditions.

[0036] The intelligent copper wire bonding equipment cluster control method based on environmental IoT specifically includes the following steps:

[0037] S1: Obtain historical data of cluster production control and extract monitoring features based on monitoring categories to form cluster production control parameter feature data.

[0038] Acquire historical production control data of the cluster and extract monitoring features based on monitoring categories to form cluster production control parameter feature data, including: acquiring historical production control data of the cluster and extracting features based on adjustment control parameters to form regulation parameter feature data; acquiring historical production control data of the cluster and extracting features based on capacity control parameters to form capacity control parameter feature data.

[0039] The feature information of control parameters in the production process of bonding copper wire was extracted. Two types of control parameters were mainly considered: one type is the control parameter that determines the quality of the bonding copper wire in the production process, and the other type is the control parameter that affects the production capacity of the bonding copper wire. By extracting features from these two types of control parameters, it is possible to achieve in-process quality control of bonding copper wire, so as to ensure the quality of bonding copper wire by adjusting parameters in real time. It is also possible to achieve output control and monitoring, so as to avoid large fluctuations in output caused by changes in control parameters, which may result in failure to meet production demand.

[0040] Historical production control data of the cluster is acquired, and feature extraction based on adjustment control parameters is performed to form control parameter feature data. This includes: using the performance parameters of the bonding copper wire as a reference, extracting historical control parameters for all production projects with the same performance parameters of the bonding copper wire from the historical production control data of the cluster; for different adjustment control parameters, performing the following feature extraction processing based on data type according to the corresponding historical control parameters to form corresponding control parameter features: for continuous data of adjustment control parameters, determining the minimum and maximum values ​​based on the corresponding historical control parameters to form continuous control parameter features; for discrete data of adjustment control parameters, determining the minimum, maximum, minimum acquisition time interval, and maximum acquisition time interval based on the corresponding historical control parameters to form discrete control parameter features; and combining the control parameter features of all adjustment control parameters to form a control parameter feature set, and calibrating the performance parameters of the bonding copper wire.

[0041] The feature information extraction of adjustment control parameters that determine the production quality of bonded copper wire considers the types of parameter data collected for different adjustment control parameters. Different types of data have different aspects and focuses for feature extraction, aiming to accurately and reasonably reflect the characteristics of the control parameters. The parameter data acquired for adjustment control parameters in the bonded copper wire production process mostly fall into two categories: continuous and discrete data types. For continuous data types, the main focus is on obtaining the maximum and minimum values ​​that can be determined from historical data to establish a reasonable range. Of course, for historical large datasets, deviations in the maximum and minimum values ​​can be considered. Therefore, preprocessing can be performed before determining the maximum and minimum values ​​to eliminate the impact of a few data deviations on value extraction. For discrete data types, although there is also a certain reasonable numerical range, the time characteristics of discrete data acquisition are also considered. Therefore, the time interval between adjacent data acquisitions is statistically analyzed for maximum and minimum values ​​to form an effective and reasonable acquisition time interval feature. Similarly, for extracting extreme values, preprocessing the initial large dataset to eliminate deviations is also necessary. In addition, it should be noted that, considering the application scenarios and other factors, the performance parameters of the bonding copper wire may differ each time it is produced. Therefore, the range of adjustment and control parameters should be analyzed and processed using big data from the same bonding copper wire production project to avoid significant deviations in the obtained characteristics due to differences in the performance parameters of the bonding copper wire.

[0042] Historical production control data of the cluster is acquired, and features based on capacity control parameters are extracted to form capacity control parameter feature data. This includes: using the performance parameters of the bonding copper wire as a reference, extracting the historical capacity parameters of all capacity control parameters in production projects with the same bonding copper wire performance parameters from the historical production control data of the cluster; arranging different capacity control parameters sequentially according to the production process to form a production control parameter sequence set; determining the effective capacity fluctuation range of each capacity control parameter in the capacity control parameter sequence set for different production projects with the same bonding copper wire performance parameters, forming the process capacity project relationship feature corresponding to the capacity control parameter sequence set; and performing a union operation on the effective capacity fluctuation range of the same capacity control parameter in the process capacity project relationship feature for all production projects with the same bonding copper wire performance parameters to form the process capacity relationship feature corresponding to the bonding copper wire performance parameters.

[0043] For capacity control parameters, capacity is affected by both process steps and variations in control parameters. Therefore, when extracting features, classifying them based on the performance parameters of the bonding copper wire yields more reasonable feature data. Furthermore, since the final output of bonding copper wire is gradually achieved through a sequential arrangement of processes from raw materials, different processes inevitably damage some of the raw materials. Therefore, when obtaining features for capacity control parameters, the entire process flow is considered. In other words, capacity control parameters are expressed as a capacity chain formed by the sequence of processes that transfer capacity.

[0044] S2: Collect real-time production control data from the cluster and combine it with cluster production control characteristic data to perform production status analysis and generate real-time production status monitoring results data.

[0045] Collect real-time production control data from the cluster and combine it with cluster production control characteristic data to analyze the production status and generate real-time production status monitoring results data. This includes: collecting real-time control parameters of adjustment control parameters and combining them with control parameter characteristic data to analyze the control production status and generate control production status monitoring results information; collecting real-time capacity control parameters of capacity control parameters and combining them with capacity control parameter characteristic data to analyze capacity production status and generate capacity production status monitoring results information.

[0046] The collected real-time monitoring data is compared with large datasets to determine whether it represents the appropriate parameters for control. Therefore, comparing real-time data with corresponding feature data allows for accurate, efficient, and reasonable real-time monitoring, ensuring effective and timely production control monitoring of the cluster equipment.

[0047] Real-time control parameters of adjustment and control types are collected and combined with characteristic data of the control parameters to analyze the control production status and generate real-time monitoring results of the control production status. This includes: collecting real-time control parameters of adjustment and control types; performing the following real-time self-check analysis on two real-time control parameters collected for the same adjustment and control type parameter: if both real-time control parameters are empty, and other adjustment and control types have collected valid data, a control acquisition fault warning is generated; if both real-time control parameters are empty, and other adjustment and control types have not collected valid data, a non-working status is generated; if either of the two real-time control parameters is empty, and the other real-time control parameter belongs to the corresponding control parameter... If the control parameter features corresponding to the control parameter feature set are used, a single effective control information is formed, and the real-time control parameter belonging to the control parameter feature corresponding to the control parameter feature set is output. If either of the two real-time control parameters is a null parameter, and the other real-time control parameter does not belong to the control parameter feature corresponding to the control parameter feature set, a dual control acquisition fault warning is formed. If the difference between the two real-time control parameters exceeds the comparison error threshold, a dual control acquisition error exceeding the limit warning is formed. If the difference between the two real-time control parameters does not exceed the comparison error threshold, and both belong to the control parameter feature corresponding to the control parameter feature set, a dual control acquisition normal information is formed, and the average real-time control parameter is output.

[0048] For real-time data comparison of adjusted control parameters, unlike other data acquisition systems, this application, based on data collected by the Environmental Internet of Things (IoT), considers both the rationality of real-time data acquisition and the fact that the Environmental IoT, while utilizing environmental energy for data collection, saves energy and reduces data acquisition costs. The validity and correctness of the collected data can be verified by collecting the same control parameter from two collection points. Compared to traditional data acquisition systems, this does not significantly increase costs, but provides a more effective analytical reference for judging the correctness and rationality of real-time data acquisition. It should be noted that the comparative analysis will differ depending on the conditions of the two real-time data points collected. When both real-time parameters are empty, the acquisition status of other control parameters is considered. If only the analyzed parameter is empty, it indicates an error in data extraction. If all control parameters are empty, it can be determined that the current cluster equipment has not started production. For two collected real-time parameters, if one has a parameter and the other does not, it is necessary to compare and analyze the acquired parameters using features extracted from big data. If the parameter falls within the feature range, the one collection point without extracted parameters is determined to be faulty, while the other is valid. If the collected parameter does not fall within the feature range, it proves that both collection points have failed. If both collection points have numerical data for their real-time parameters, but the difference between the two parameters is too large, it also proves that the collection point is faulty. If the difference between the two parameters is small and both fall within the corresponding feature range, the average parameter value can be used to represent the real-time parameter of the corresponding parameter. By comparing the parameters collected from two collection points with their corresponding feature ranges, the correctness of the collected data can be further accurately determined. Furthermore, the collected data can be used to determine whether production is proceeding, expanding the scope of parameter analysis and making the analysis more comprehensive and accurate.

[0049] For real-time control parameters to belong to the corresponding control parameter feature set, the following conditions must be met: If the real-time control parameter is continuous data, then the value of the real-time control parameter belongs to the numerical range of the corresponding continuous control parameter feature; if the real-time control parameter is discrete data, then the real-time control parameter ensures that the value of the real-time control parameter belongs to the numerical range of the corresponding continuous control parameter feature, and the range of the real-time control parameter acquisition time interval belongs to the acquisition time interval range of the corresponding continuous control parameter feature.

[0050] When determining whether real-time data acquired for the same control parameter falls within the corresponding feature range, the data type needs to be considered. For continuous data, it is sufficient to determine that the acquired real-time parameter falls within the range established by the extreme values. However, for discrete data, it is necessary to determine not only that the parameter values ​​fall within the range established by the corresponding extreme values, but also that the range of the acquisition time interval falls within the range established by the corresponding extreme values.

[0051] Real-time capacity control parameters of capacity control categories are collected, and combined with the characteristic data of capacity control parameters, capacity production status analysis is performed to form real-time monitoring results of capacity production status. This includes: determining the real-time capacity control range corresponding to each capacity control category parameter based on the collected real-time capacity control parameters; arranging the real-time capacity control ranges of different capacity control categories parameters according to the process sequence to form real-time process capacity relationship data; determining the process capacity relationship characteristics corresponding to the real-time process capacity relationship data based on the performance parameters of the bonding copper wire, and combining this with the real-time monitoring results of the production status to perform capacity status analysis and form real-time monitoring results of capacity production status.

[0052] After obtaining the real-time capacity control parameters, it is necessary to track the changes in output for each step of the entire process. Therefore, the real-time capacity control parameters are also arranged according to the process sequence and compared with the corresponding bonding copper wire performance parameters to achieve reasonable and accurate judgment.

[0053] Based on the performance parameters of the bonding copper wire, the process capacity relationship characteristics corresponding to the real-time capacity relationship data of different processes are determined. Combined with the real-time monitoring results of production status control, capacity status analysis is performed to form real-time monitoring results of production status, including: if the different real-time capacity control ranges in the process capacity relationship data all fall within the effective fluctuation range of the corresponding capacity control parameters in the process capacity relationship characteristic data, then normal capacity control information is generated; if the real-time capacity control ranges in the process capacity relationship data all fall within the effective fluctuation range of the corresponding capacity control parameters in the process capacity relationship characteristic data, and all real-time control parameters under the corresponding process are normal, then non-control-related capacity anomaly warning information is generated; if the real-time capacity control ranges in the process capacity relationship data all fall within the effective fluctuation range of the corresponding capacity control parameters in the process capacity relationship characteristic data, and the real-time control parameters under the corresponding process are abnormal, then control-related capacity anomaly warning information is generated.

[0054] During analysis and judgment, different comparison results between real-time capacity parameters and relational characteristics will lead to different capacity status assessments. If each real-time capacity parameter falls within its corresponding capacity characteristic range, the entire production process is stable, and output consistently meets demand. If real-time capacity parameters do not fall within their corresponding capacity characteristic range, and the corresponding process's adjustment control parameters are confirmed to not fall outside this range, then the abnormal capacity impact may be due to changes in non-adjustment control parameters such as material transfer. Conversely, if real-time capacity parameters do not fall within their corresponding capacity characteristic range, and the corresponding process's adjustment control parameters are confirmed to fall outside this range, then the abnormal capacity may be caused by abnormal adjustment control parameters. For different types of anomaly assessments, targeted production control adjustments can be made, significantly improving the timeliness and accuracy of production control.

[0055] This invention also provides a cluster control system for intelligent bonding copper wire equipment based on the Internet of Things (IoT). The system includes: an A-IoT acquisition terminal, comprising A-IoT acquisition terminals of different energy acquisition types, installed on corresponding process equipment, acquiring different real-time control parameters and real-time production capacity control parameters corresponding to the process; for A-IoT acquisition terminals acquiring different real-time control parameters, the acquisition of each adjustment control parameter is completed by two identical A-IoT acquisition terminals; an A-IoT data acquisition unit, used to collect real-time parameters acquired by all A-IoT acquisition terminals; an A-IoT database, used to store historical data of cluster production control; and an A-IoT data processing and feedback unit, used to acquire historical data of cluster production control from the A-IoT database, perform feature extraction to form cluster production control parameter feature data, and extract real-time parameters acquired by the A-IoT data acquisition unit, combining the cluster production control parameter feature data to perform production status analysis, forming real-time production status monitoring result data and issuing early warnings.

[0056] This system utilizes environmental IoT-type data acquisition terminals to collect data from the surrounding environment, fully leveraging the diffused energy generated during copper wire bonding production and improving energy efficiency. Data collected at each location is uploaded via low-power transmission, ensuring stability and reliability and avoiding signal degradation caused by obstructions. A database stores historical data appropriately, providing a foundation for big data analysis. The processing and feedback units promptly extract feature data and perform real-time data comparison and analysis to identify anomalies, enabling effective and accurate control of cluster production and ensuring the quality of the bonded copper wire. The entire system tightly integrates data acquisition, processing, and analysis to form a complete production control and monitoring system, providing an intelligent data foundation for meeting the high-precision, high-quality production control requirements of copper wire bonding.

[0057] In summary, the beneficial effects of the intelligent copper wire bonding equipment cluster control method and system based on environmental Internet of Things provided in the embodiments of the present invention are as follows:

[0058] This method utilizes the Internet of Things (IoT) in the environment to construct a production monitoring and control system for a cluster of copper bonding equipment. On one hand, it performs feature analysis based on historical big data on the control parameters to be monitored, determining the reasonable range of these parameters during production. On the other hand, it provides accurate and reasonable parameter comparison features for subsequent real-time control monitoring and analysis, ensuring accurate and effective real-time monitoring and control of the control parameters. This effectively guarantees the production quality of the bonded copper wire. Furthermore, by employing the IoT for data collection and analysis, and leveraging the energy of the surrounding environment as a primary energy source, this method further optimizes the energy generated by the equipment cluster during production, adding a new element to the concept of a smart factory and further improving energy utilization efficiency, resulting in energy conservation and environmental protection. Moreover, because the IoT utilizes low-power information transmission, it can effectively transmit and disseminate information in complex equipment cluster areas without significant signal attenuation due to terrain or equipment installation conditions.

[0059] This system utilizes environmental IoT-type data acquisition terminals to collect data from the surrounding environment, fully leveraging the diffused energy generated during copper wire bonding production and improving energy efficiency. Data collected at each location is uploaded via low-power transmission, ensuring stability and reliability and avoiding signal degradation caused by obstructions. A database stores historical data appropriately, providing a foundation for big data analysis. The processing and feedback units promptly extract feature data and perform real-time data comparison and analysis to identify anomalies, enabling effective and accurate control of cluster production and ensuring the quality of the bonded copper wire. The entire system tightly integrates data acquisition, processing, and analysis to form a complete production control and monitoring system, providing an intelligent data foundation for meeting the high-precision, high-quality production control requirements of copper wire bonding.

[0060] In the embodiments of this application, "instruction" can include direct and indirect instructions, as well as explicit and implicit instructions. The information indicated by a certain piece of information is called the information to be instructed. In the specific implementation process, there are many ways to instruct the information to be instructed, such as, but not limited to, directly instructing the information to be instructed, such as the information to be instructed itself or its index. It can also indirectly instruct the information to be instructed by instructing other information, where there is a relationship between the other information and the information to be instructed. It can also instruct only a part of the information to be instructed, while the other parts are known or pre-agreed upon. For example, the instruction of specific information can be achieved by using a pre-agreed (e.g., protocol-defined) arrangement of various pieces of information, thereby reducing instruction overhead to some extent. At the same time, common parts of various pieces of information can be identified and uniformly indicated to reduce the instruction overhead caused by individually indicating the same information.

[0061] Furthermore, the specific indication method can also be any existing indication method, such as, but not limited to, the above-mentioned indication methods and their various combinations. Specific details of various indication methods can be found in existing technologies, and will not be repeated here. As described above, for example, when multiple pieces of information of the same type need to be indicated, the indication methods for different pieces of information may differ. In the specific implementation process, the required indication method can be selected according to specific needs. This application embodiment does not limit the selected indication method; therefore, the indication methods involved in this application embodiment should be understood to cover various methods that enable the party to be indicated to obtain the information to be indicated.

[0062] It should be understood that the information to be indicated can be sent as a whole or divided into multiple sub-information messages sent separately, and the sending period and / or timing of these sub-information messages can be the same or different. The specific sending method is not limited in this application embodiment. The sending period and / or timing of these sub-information messages can be predefined, for example, according to a protocol, or configured by the sending device by sending configuration information to the receiving device.

[0063] "Predefined" or "pre-configured" can be achieved by pre-saving corresponding codes, tables, or other means that can be used to indicate relevant information in the device. This application does not limit the specific implementation method. "Saving" can refer to saving in one or more memories. These memories can be separate installations or integrated into the encoder, decoder, processor, or communication device. Alternatively, some memories can be separately installed, while others are integrated into the decoder, processor, or communication device. The type of memory can be any form of storage medium, and this application does not limit this.

[0064] The “protocol” mentioned in this application embodiment may refer to a protocol family in the field of communication, a standard protocol with a similar protocol family frame structure, or a related protocol applied to future communication systems. This application embodiment does not specifically limit this.

[0065] In the embodiments of this application, descriptions such as "when," "under the circumstances," "if," and "if" all refer to the device making corresponding processing under certain objective circumstances, and are not limited to a specific time. They do not require the device to make a judgment action during implementation, nor do they imply any other limitations.

[0066] In the description of the embodiments of this application, unless otherwise stated, " / " indicates that the objects before and after are in an "or" relationship. For example, A / B can represent A or B. "And / or" in the embodiments of this application is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone, where A and B can be singular or plural. Furthermore, in the description of the embodiments of this application, unless otherwise stated, "multiple" refers to two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple. Additionally, to facilitate a clear description of the technical solutions of the embodiments of this application, the terms "first" and "second" are used in the embodiments of this application to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first," "second," etc., do not limit the quantity or order of execution, and that "first," "second," etc., are not necessarily different. Furthermore, in the embodiments of this application, words such as "exemplary" or "for example" are used to indicate that something is being used as an example, illustration, or description. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner for ease of understanding.

[0067] It should be understood that the processor in the embodiments of this application can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc.

[0068] It should also be understood that the memory in the embodiments of this application can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0069] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0070] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0071] In this application, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0072] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0073] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0074] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0075] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0076] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0077] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0078] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0079] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A cluster control method for intelligent copper wire bonding equipment based on environmental Internet of Things, characterized in that, include: Acquire historical production control data for the cluster and extract monitoring features based on monitoring categories to form cluster production control parameter feature data; Collect real-time production control data from the cluster and combine it with cluster production control characteristic data to analyze production status and generate real-time production status monitoring results data. The process of acquiring historical production control data for the cluster and extracting monitoring features based on monitoring categories to form cluster production control parameter feature data includes: The historical production control data of the cluster is acquired, and feature extraction based on adjustment control parameters is performed to form control parameter feature data. The historical production control data of the cluster is obtained, and feature extraction based on capacity control parameters is performed to form capacity control parameter feature data. The process of acquiring the historical production control data of the cluster, performing feature extraction based on adjustment control parameters, and forming control parameter feature data includes: Using the performance parameters of the bonding copper wire as a reference, extract the historical control parameters of all adjustment control parameters in production projects with the same performance parameters of the bonding copper wire from the historical production control data of the cluster. For different adjustment and control parameters, the following feature extraction processes based on data type are performed according to the corresponding historical control parameters to form the corresponding control parameter features: The data collected for the adjustment and control parameters are continuous data. Based on the corresponding historical control parameters, the minimum and maximum values ​​are determined to form the continuous characteristics of the control parameters. The data collected for the adjustment and control parameters are discrete data. Based on the corresponding historical control parameters, the minimum value, maximum value, minimum acquisition time interval and maximum acquisition time interval are determined to form the discrete characteristics of the control parameters. The control parameter characteristics of all adjustment and control parameters are collected to form a control parameter characteristic set, and the performance parameters of the bonding copper wire are calibrated. The process of acquiring the historical production control data of the cluster and extracting features based on capacity control parameters to form capacity control parameter feature data includes: Using the performance parameters of the bonding copper wire as a reference, extract the historical capacity parameters of all capacity control parameters in production projects with the same performance parameters of the bonding copper wire from the historical production control data of the cluster. Different capacity control parameters are arranged sequentially according to the production process to form a capacity control parameter sequence set. Taking production projects as units, determine the effective capacity fluctuation range of each capacity control parameter in the sequence set of capacity control parameters in different production projects under the same bonding copper wire performance parameters, and form the process capacity project relationship characteristics corresponding to the sequence set of capacity control parameters. For all production projects with the same performance parameters of the bonding copper wires, the effective capacity fluctuation range of the same capacity control parameter in the process capacity project relationship feature is combined to form the process capacity relationship feature corresponding to the performance parameters of the bonding copper wires.

2. The intelligent copper wire bonding equipment cluster control method based on environmental Internet of Things as described in claim 1, characterized in that, The process involves collecting real-time production control data from the cluster and combining it with cluster production control characteristic data to perform production status analysis, generating real-time production status monitoring results data, including: Real-time control parameters of control parameters are collected and adjusted, and combined with the characteristic data of the control parameters, the control production status is analyzed to form real-time monitoring results of the control production status. Real-time capacity control parameters of capacity control parameters are collected, and combined with the characteristic data of the capacity control parameters, capacity production status analysis is performed to form real-time monitoring results of capacity production status.

3. The cluster control method for intelligent bonding copper wire equipment based on environmental Internet of Things as described in claim 2, characterized in that, The real-time control parameters of the collected and adjusted control parameters are combined with the characteristic data of the control parameters to analyze the control production status and form real-time monitoring result information of the control production status, including: The real-time control parameters of the same adjustment control parameter are collected and adjusted. The following real-time self-check analysis is performed on two real-time control parameters collected from the same adjustment control parameter: If the real-time control parameters mentioned in the two places are empty parameters, and other adjustment and control parameters have collected valid data, then a control acquisition fault warning information is generated. If the real-time control parameters mentioned in both places are empty parameters, and no valid data is collected for other adjustment and control parameters, then a non-working status information is generated; If either of the two real-time control parameters is an empty parameter, and the other real-time control parameter belongs to the corresponding control parameter feature in the set of control parameter features, then a single effective control information is formed, and the real-time control parameter belonging to the corresponding control parameter feature in the set of control parameter features is output. If either of the two real-time control parameters is an empty parameter, and the other real-time control parameter does not belong to the corresponding control parameter feature in the set of control parameter features, then a dual control acquisition fault warning information is formed. If the difference between the two real-time control parameters exceeds the comparison error threshold, a dual control acquisition error over-limit warning message will be generated. If the difference between the two real-time control parameters does not exceed the comparison error threshold, and both belong to the corresponding control parameter features in the control parameter feature set, then dual control is formed to collect normal information, and the average real-time control parameter is output.

4. The intelligent copper wire bonding equipment cluster control method based on environmental Internet of Things as described in claim 3, characterized in that, The real-time control parameter belongs to the control parameter feature corresponding to the control parameter feature set, and must meet the following conditions: If the real-time control parameter is continuous data, then the value of the real-time control parameter belongs to the numerical range of the corresponding continuous characteristic of the control parameter. If the real-time control parameter is discrete data, then the real-time control parameter ensures that the value of the real-time control parameter belongs to the numerical range of the corresponding continuous feature of the control parameter, and the range of the acquisition time interval of the real-time control parameter belongs to the acquisition time interval range of the corresponding continuous feature of the control parameter.

5. The intelligent copper wire bonding equipment cluster control method based on environmental Internet of Things according to claim 4, characterized in that, The real-time capacity control parameters of the collected capacity control parameters are combined with the characteristic data of the capacity control parameters to analyze the capacity production status and form real-time monitoring results of capacity production status, including: Based on the real-time capacity control parameters of the collected capacity control parameters, determine the real-time capacity control range corresponding to each capacity control parameter. The real-time capacity control ranges of different capacity control parameters are arranged according to the process sequence to form real-time process capacity relationship data; Based on the performance parameters of the bonding copper wire, the process capacity relationship characteristics corresponding to the real-time relationship data of the capacity of different processes are determined, and the capacity status analysis is carried out in combination with the real-time monitoring results of the production status regulation to form real-time monitoring results data of production capacity status.

6. The cluster control method for intelligent bonding copper wire equipment based on environmental Internet of Things as described in claim 5, characterized in that, The process capacity relationship characteristics corresponding to the real-time capacity relationship data of different processes are determined based on the performance parameters of the bonding copper wire, and the capacity status analysis is performed in combination with the real-time monitoring results of the production status control to form real-time monitoring results data of production capacity status, including: If different real-time capacity control ranges in the real-time relationship data of the process capacity all fall within the effective fluctuation range of the corresponding capacity control parameter in the process capacity relationship feature data, then normal capacity control information is formed. If the real-time capacity control range in the process capacity real-time relationship data all fall within the effective fluctuation range of the corresponding capacity control parameters in the process capacity relationship feature data, and all the real-time control parameters under the corresponding process are normal, then a non-control type capacity anomaly warning information is generated. If the real-time capacity control range in the process capacity real-time relationship data all fall within the effective fluctuation range of the corresponding capacity control parameters in the process capacity relationship feature data, and the real-time control parameters under the corresponding process are abnormal, then a control-type capacity abnormality early warning information is generated.

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