Intelligent bonding copper wire equipment cluster control method and system based on environmental Internet of Things

Abstract

The invention provides an intelligent bonding copper wire equipment cluster control method and system based on environment Internet of Things, and relates to the technical field of bonding copper wire production control. The method comprises the following steps: acquiring cluster production control historical data, and performing monitoring feature extraction based on a monitoring category to form cluster production control parameter feature data; and collecting cluster production control real-time data, and performing production state analysis in combination with the cluster production control feature data to form production state real-time monitoring result data. According to the method, a monitoring system aiming at the control form of the whole bonding copper wire production cluster is constructed by utilizing the environment internet of things, so that an intelligent regulation and control data basis is provided for fully meeting the high-precision and high-quality production control requirements of the bonding copper wires.

Description

Technical Field

[0001] The present invention relates to the technical field of copper bonding wire production control, and in particular to an intelligent copper bonding wire equipment cluster control method and system based on environmental Internet of Things. Background Art

[0002] Bonding copper wire is the main material of semiconductor packaging. Qualified bonding copper wire can effectively guarantee the quality of packaging, so the production control of bonding copper wire needs to be accurate and efficient. At present, with the advancement of science and technology, the Internet of Things is gradually becoming popular, especially compared with the environmental Internet of Things, which can rely on environmental energy to perform its functions and has a low-power and low-consumption signal transmission method, which plays a more efficient and convenient role in production control and monitoring.

[0003] However, the current environmental Internet of Things is not systematic enough in production control monitoring, and cannot integrate data resources for reasonable real-time processing of production control. Especially for production processes such as bonding copper wires that require high precision and high quality, it is necessary to reasonably coordinate and efficiently control the entire production cluster equipment to effectively ensure the quality of bonding copper wires.

[0004] Therefore, it is an urgent problem to be solved to design an intelligent bonding copper wire equipment cluster control method and system based on the environmental Internet of Things, and to use the environmental Internet of Things to build a monitoring system for the control form of the entire bonding copper wire production cluster, thereby providing a data basis for intelligent regulation to fully meet the high-precision and high-quality production control requirements of bonding copper wire. Summary of the invention

[0005] The purpose of the present invention is to provide an intelligent copper wire bonding equipment cluster control method based on the environmental Internet of Things. By using the environmental Internet of Things to construct a production monitoring and control system for the copper wire bonding equipment cluster, on the one hand, the control parameters to be monitored are analyzed based on historical big data to determine the reasonable range of the control parameters in the production process. On the other hand, it is also to provide accurate and reasonable parameter comparison basic features for subsequent real-time control monitoring and analysis, to ensure that the real-time monitoring and control of the control parameters is accurate and effective, so that the production quality of the copper wire bonding can be effectively guaranteed. In addition, since the environmental Internet of Things is used to collect and analyze data, combined with the environmental Internet of Things, the energy generated by the equipment cluster in the production process can be further reasonably utilized, which adds new elements to the concept of smart factories, further improves the utilization rate of energy in production, and has the effect of energy saving and environmental protection. Of course, since the environmental Internet of Things uses a low-power information transmission method, it can effectively transmit and spread information in complex equipment cluster areas without greatly reducing the signal of information transmission due to regional terrain and equipment installation conditions.

[0006] The purpose of the present invention is also to provide an intelligent copper bonding wire equipment cluster control system based on the environmental Internet of Things. The system realizes the purpose of utilizing the surrounding environment energy for data collection by setting up an environmental Internet of Things type collection terminal, fully realizing the utilization of the diffusion energy generated by the production of the copper bonding wire, and improving the utilization rate of energy in the production process. The data collected at each location point is uploaded through a low-power data transmission method, which is stable and reliable, and can avoid the situation where the signal is poor due to object obstruction. The database completes reasonable historical data storage and provides a data basis for big data analysis, while the processing and feedback unit can timely complete the feature data extraction and real-time data comparative analysis to feedback abnormal situations to achieve effective and accurate control of cluster production, and effectively ensure the quality of the copper bonding wire. The entire system closely links data collection to processing and analysis to form a complete system for production control and monitoring, which fully meets the high-precision and high-quality production control requirements of the copper bonding wire and provides a data basis for intelligent regulation.

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

[0008] In the present invention, the method uses the environmental Internet of Things to construct a production monitoring and control system for the bonding copper wire equipment cluster. On the one hand, the control parameters to be monitored are analyzed based on historical big data to determine the reasonable range of the control parameters in the production process. On the other hand, it also provides accurate and reasonable basic characteristics for parameter comparison for subsequent real-time control monitoring and analysis, ensuring that the real-time monitoring and control of the control parameters is accurate and effective, so that the production quality of the bonding copper wire can be effectively guaranteed. In addition, since the environmental Internet of Things is used to collect and analyze data, combined with the environmental Internet of Things, the energy of the surrounding environment can be used as a form of senior energy, and the energy generated by the equipment cluster in the production process is further reasonably utilized, which adds new elements to the concept of smart factories, further improves the utilization rate of energy in production, and has the effect of energy saving and environmental protection. Of course, since the environmental Internet of Things uses a low-power information transmission method, it can effectively transmit and spread information in complex equipment cluster areas without greatly reducing the signal of information transmission due to regional terrain and equipment installation conditions.

[0009] As a possible implementation method, historical data of cluster production control is obtained, and monitoring features are extracted based on monitoring categories to form characteristic data of cluster production control parameters, including: obtaining historical data of cluster production control, extracting features based on adjustment control parameters, and forming characteristic data of regulation parameters; obtaining historical data of cluster production control, extracting features based on capacity control parameters, and forming characteristic data of capacity control parameters.

[0010] In the present invention, characteristic information of control parameters in the production process of bonding copper wire is extracted, and two types of control parameters are mainly considered. One is the control parameter that determines the production quality of bonding copper wire in the production process, and the other is the control parameter that affects the production capacity of bonding copper wire. Feature extraction is performed on these two types of control parameters, which can realize in-process quality control in the production process of bonding copper wire, and ensure the quality of bonding copper wire by real-time control and adjustment of parameters. It can also realize control and monitoring of output to avoid the situation where large fluctuations in output caused by changes in control parameters and cannot meet production output requirements.

[0011] As a possible implementation method, historical data of cluster production control is obtained, and feature extraction based on adjustment control parameters is performed to form control parameter feature data, including: taking bonding copper wire performance parameters as a reference, extracting historical control parameters of all adjustment control parameters in production projects with the same bonding copper wire performance parameters in the historical data of cluster production control; for different adjustment control parameters, the following feature extraction processing based on data type is performed according to the corresponding historical control parameters to form corresponding control parameter features: for data collected for adjustment control parameters that are continuous data, minimum and maximum values ​​are determined according to the corresponding historical control parameters to form continuous features of control parameters; for data collected for adjustment control parameters that are discrete data, minimum and maximum values ​​are determined according to the corresponding historical control parameters to form discrete features of control parameters; the control parameter features of all adjustment control parameters are aggregated to form a control parameter feature set, and the performance parameters of bonding copper wire are calibrated.

[0012] In the present invention, the feature information extraction of the adjustment control parameters that determine the production quality of the bonding copper wire takes into account the types of parameter data collected corresponding to different adjustment control parameters. After all, different types of data have different aspects and focuses on feature extraction, so as to accurately and reasonably reflect the characteristics of the control parameters. Most of the parameter data corresponding to the adjustment control parameters of the production process of the bonding copper wire are of two categories, one is a continuous data type and the other is a discrete data type. For the continuous data type, it is mainly to obtain the maximum and minimum values ​​that can be determined under the historical data, and then determine a reasonable range. Of course, for historical big data, the maximum and minimum values ​​can also consider the situation of data deviation. Therefore, before determining the maximum and minimum values, preprocessing can be performed to exclude the influence of a few data deviations on value extraction. For discrete data types, although they also have a certain reasonable numerical range, considering that discrete data collection also has certain time characteristics, the time interval between adjacent data collections is also counted for maximum and minimum values ​​to form an effective and reasonable collection time interval feature. Similarly, for the case of extracting extreme values, it is also necessary to preprocess the initial big data to exclude deviations. In addition, it should be noted that considering the usage scenarios of the bonding copper wire and other situations, the performance parameters of the bonding copper wire determined each time the bonding copper wire is produced may be different. Therefore, the parameter range of the adjustment control parameters obtained should also be analyzed and processed by big data from the same bonding copper wire production project to avoid large deviations in the characteristics obtained due to different performance parameters of the bonding copper wire.

[0013] As a possible implementation method, historical data of cluster production control is obtained, and feature extraction based on capacity control parameters is performed to form feature data of capacity control parameters, including: taking the performance parameters of 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 in the historical data of cluster production control; arranging different capacity control parameters in sequence according to the production process to form a production control parameter sequence set; taking production projects as units, determining the effective capacity fluctuation range of each capacity control parameter in the capacity control parameter sequence set in different production projects under the same bonding copper wire performance parameters, and forming the process capacity project relationship characteristics corresponding to the capacity control parameter sequence set; for all production projects with the same bonding copper wire performance parameters, performing a combined operation on the effective capacity fluctuation range of the same capacity control parameter in the process capacity project relationship characteristics, and forming the process capacity relationship characteristics corresponding to the bonding copper wire performance parameters.

[0014] In the present invention, for capacity control parameters, on the one hand, the capacity is affected by the process, and on the other hand, it will be different due to the change of the adjustment control parameters. Therefore, when extracting features, the bonding copper wire performance parameters are still used as a classification method to determine more reasonable feature data. Of course, for capacity control parameters, since the final output of bonding copper wire is gradually formed from the raw materials through sequentially arranged processes, different processes in this process will more or less damage some raw materials. Therefore, when obtaining the characteristics of capacity control parameters, it is done from the entire process. That is, the parameters of the capacity control parameters are expressed in the process sequence of capacity transmission to form a capacity chain.

[0015] As a possible implementation method, real-time data of cluster production control is collected, and combined with cluster production control characteristic data, production status analysis is performed to form real-time monitoring result data of production status, including: collecting real-time control parameters of adjustment control parameters, and combining with control parameter characteristic data, to perform control production status analysis, and form real-time monitoring result information of control production status; collecting real-time capacity control parameters of capacity control parameters, and combining with capacity control parameter characteristic data, to perform capacity production status analysis, and form real-time monitoring result information of capacity production status.

[0016] In the present invention, the collected real-time monitoring data is also completed by comparing the gap between the real-time monitoring data and the big data in order to determine whether it is the reasonable parameter data required for control. Therefore, the use of real-time data and corresponding feature data for comparative analysis can accurately, reasonably and efficiently complete real-time monitoring, ensuring real-time and effective production control monitoring of cluster equipment.

[0017] As a possible implementation method, real-time control parameters of adjustment control parameters are collected, and combined with characteristic data of control parameters, the control production status analysis is performed to form real-time monitoring result information of the control production status, including: collecting real-time control parameters of adjustment control parameters, and performing the following real-time self-check analysis on two real-time control parameters collected from the same adjustment control parameter: if the two real-time control parameters are empty parameters, and valid data has been collected for other adjustment control parameters, then control collection failure warning information is generated; if the two real-time control parameters are empty parameters, and no valid data has been collected for other adjustment control parameters, then non-working status information is generated; if any one of the two real-time control parameters is an empty parameter, and the other real-time control parameter is The corresponding control parameter feature in the corresponding control parameter feature set forms a single effective control information, and outputs the real-time control parameter of the corresponding control parameter feature in the corresponding control parameter feature set; if any one 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 corresponding control parameter feature set, then a double control acquisition fault warning information is formed; if the difference between the two real-time control parameters exceeds the comparison error threshold, then a double control acquisition error over-limit warning information is formed; 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 corresponding control parameter feature set, then a double control acquisition normal information is formed, and the average real-time control parameter is output.

[0018] In the present invention, for the comparison of real-time data of adjusting control parameters, it is different from other data acquisition systems. Based on the data collected by the environmental Internet of Things, this application considers the rationality of real-time data collection on the one hand, and on the other hand, it also considers that the environmental Internet of Things saves energy and reduces the cost of data collection while collecting data with the help of environmental energy. Whether the collected data is valid and correct can be achieved by collecting the same control parameter at two collection points. Compared with the traditional data acquisition system, there will be no huge cost increase, but it does have a more effective analysis reference in judging the correctness and rationality of real-time data collection. It should be noted that when performing comparative analysis, it will be different due to the situation of the two real-time data collected. When two real-time parameters are empty parameters, consider the collection of other control parameters. If only the analyzed parameters are empty parameters, it means that an error has occurred in extracting data. If all control parameters are empty parameters, it can be determined that the current cluster equipment has not started working and production. For two collected real-time parameters, if one has parameters and the other does not, it is necessary to compare and analyze the acquired parameters in combination with the features extracted from big data. If the parameters belong to the feature range, it is determined that one of the acquisition points without extracted parameters is faulty and the other is valid. If the collected parameters do not belong to the feature range, it proves that both acquisition points have failed. If the real-time parameters acquired by the two acquisition points have numerical data and the difference between the two parameters is too large, it also proves that the acquisition point is faulty. If the difference between the two parameters is not large and both belong to the corresponding feature range, the average parameter value can be used to refer to the real-time parameters of the corresponding parameters. By comparing the parameters collected by the two acquisition points with each other and combining them with the corresponding feature range, on the one hand, the correctness of the collected data can be further accurately determined, and on the other hand, the collected data can be used to determine whether production is in progress, which expands the content of the collected parameter analysis and judgment, making the analysis more comprehensive and accurate.

[0019] As a possible implementation method, the real-time control parameter belongs to the corresponding control parameter feature in the corresponding control parameter feature set, and the following conditions must be met: if the real-time control parameter is continuous data, the value of the real-time control parameter belongs to the value range of the corresponding continuous feature of the control parameter; if the real-time control parameter is discrete data, the real-time control parameter ensures that the value of the real-time control parameter belongs to the value range of the corresponding continuous feature of the control parameter, and the range of the collection time interval of the real-time control parameter belongs to the collection time interval range of the corresponding continuous feature of the control parameter.

[0020] In the present invention, when judging whether the real-time data acquired for the same control parameter belongs to the corresponding characteristic range, it is necessary to determine the data type. For continuous data, it is sufficient to determine whether the acquired real-time parameter belongs to the range established by the extreme value, while for discrete data, it is necessary not only to determine whether the parameter value is within the range established by the corresponding extreme value, but also to determine whether the range of the acquired time interval also belongs to the range established by the corresponding extreme value.

[0021] As a possible implementation method, 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 result information of the capacity production status, including: determining the real-time capacity control range corresponding to each capacity control parameter according to the collected real-time capacity control parameters of the capacity control parameters; arranging the real-time capacity control ranges of different capacity control parameters according to the process sequence to form process capacity real-time relationship data; determining the process capacity relationship characteristics corresponding to the real-time relationship data of different process capacities according to the performance parameters of the bonding copper wire, and performing capacity status analysis in combination with the real-time monitoring result information of the production status regulation to form real-time monitoring result data of the capacity production status.

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

[0023] As a possible implementation method, the process capacity relationship characteristics corresponding to the real-time relationship data of the capacity of different processes are determined according to the performance parameters of the bonding copper wire, and the capacity status analysis is performed in combination with the real-time monitoring result information of the control production status to form the real-time monitoring result data of the capacity production status, including: if the different real-time capacity control ranges in the process capacity real-time relationship data all belong to the effective fluctuation range of the corresponding capacity control parameters in the process capacity relationship characteristic data, then normal capacity control information is formed; if the real-time capacity control ranges in the process capacity real-time relationship data all belong to 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 type capacity abnormality warning information is formed; if the real-time capacity control ranges in the process capacity real-time relationship data all belong to the effective fluctuation range of the corresponding capacity control parameters in the process capacity relationship characteristic data, and there are abnormalities in the real-time control parameters under the corresponding process, then control type capacity abnormality warning information is formed.

[0024] In the present invention, when analyzing and judging, there will be different capacity status judgments due to different comparison results between real-time capacity parameters and relationship characteristics. If each real-time capacity parameter belongs to the corresponding capacity characteristic range, the entire production process is stable and the output is stable and reaches the demand. If the real-time capacity parameter does not belong to the corresponding capacity characteristic range, and the adjustment control parameters under the corresponding process determine that there is no situation that does not belong to the corresponding characteristic range, then it may be due to the abnormal capacity caused by the change of non-adjustment control parameters such as material transfer. If the real-time capacity parameter does not belong to the corresponding capacity characteristic range, and the adjustment control parameters under the corresponding process determine that there is a situation that does not belong to the corresponding characteristic range, then it may be due to the abnormal adjustment of the control parameters. Abnormal capacity. For different types of abnormal judgment situations, production control adjustments can be made in a targeted manner, which greatly improves the timeliness and accuracy of production control.

[0025] In the second aspect, the present invention provides an intelligent bonding copper wire equipment cluster control system based on the environmental Internet of Things, the system comprising: A-IoT acquisition terminals, including A-IoT acquisition terminals of different energy acquisition types, which are arranged on corresponding process equipment to collect different real-time control parameters and real-time production capacity control parameters corresponding to the process, and for A-IoT acquisition terminals that collect different real-time control parameters, the collection of each adjustment control parameter is completed by two identical A-IoT acquisition terminals; an A-IoT data acquisition unit, which is used to collect real-time parameters collected by all A-IoT acquisition terminals; an A-IoT database, which is used to store cluster production control historical data; an A-IoT data processing and feedback unit, which is used to obtain cluster production control historical data from the A-IoT database, perform feature extraction, and form cluster production control parameter feature data, which is used to extract the real-time parameters obtained by the A-IoT data acquisition unit, and perform production status analysis in combination with the cluster production control parameter feature data, to form real-time monitoring result data of the production status and issue an early warning.

[0026] In the present invention, the system realizes the purpose of utilizing the energy of the surrounding environment for data collection by setting up an environmental Internet of Things type collection terminal, fully realizes the utilization of the diffusion energy generated by the production of bonding copper wire, and improves the utilization rate of energy in the production process. The data collected at each location point is uploaded through a low-power data transmission method, which is stable and reliable, and can avoid the situation where the signal is poor due to object obstruction. The database completes reasonable historical data storage and provides a data basis for big data analysis, while the processing and feedback unit can timely complete the feature data extraction and real-time data comparative analysis to feedback abnormal situations to achieve effective and accurate control of cluster production, and effectively ensure the quality of bonding copper wire. The entire system closely links data collection to processing and analysis to form a complete system for production control and monitoring, which fully meets the high-precision and high-quality production control requirements of bonding copper wire and provides a data basis for intelligent regulation.

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

[0028] This method uses the environmental Internet of Things to build a production monitoring and control system for the bonding copper wire equipment cluster. On the one hand, the control parameters to be monitored are analyzed based on historical big data to determine the reasonable range of the control parameters in the production process. On the other hand, it also provides accurate and reasonable basic characteristics for parameter comparison for subsequent real-time control monitoring and analysis, ensuring that the real-time monitoring and control of the control parameters is accurate and effective, so that the production quality of the bonding copper wire can be effectively guaranteed. In addition, since the environmental Internet of Things is used to collect and analyze data, combined with the environmental Internet of Things, the energy generated by the equipment cluster in the production process can be further reasonably utilized, adding new elements to the concept of smart factories, further improving the utilization rate of energy in production, and having energy-saving and environmental protection effects. Of course, since the environmental Internet of Things uses low-power information transmission methods, it can effectively transmit and spread information in complex equipment cluster areas without greatly reducing the signal of information transmission due to regional terrain and equipment installation conditions.

[0029] The system realizes the purpose of data collection by utilizing the surrounding environment energy by setting up an environmental Internet of Things type collection terminal, fully realizing the utilization of the diffusion energy generated by the production of bonding copper wire, and improving the energy utilization rate in the production process. The data collected at each location point is uploaded through a low-power data transmission method, which is stable and reliable, and can avoid the situation where the signal is poor due to object obstruction. The database completes reasonable historical data storage and provides a data basis for big data analysis, while the processing and feedback unit can timely complete the feature data extraction and real-time data comparative analysis to feedback abnormal situations to achieve effective and accurate control of cluster production, and effectively ensure the quality of bonding copper wire. The entire system closely links data collection to processing and analysis to form a complete system for production control and monitoring, which fully meets the high-precision and high-quality production control requirements of bonding copper wire and provides a data basis for intelligent regulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments of the present invention are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0031] Figure 1 A step diagram of a method for controlling a cluster of intelligent copper bonding wire equipment based on environmental Internet of Things provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0032] The technical solutions in the embodiments of the present invention will be described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0033] Bonding copper wire is the main material of semiconductor packaging. Qualified bonding copper wire can effectively guarantee the quality of packaging, so the production control of bonding copper wire needs to be accurate and efficient. At present, with the advancement of science and technology, the Internet of Things is gradually becoming popular, especially compared with the environmental Internet of Things, which can rely on environmental energy to perform its functions and has a low-power and low-consumption signal transmission method, which plays a more efficient and convenient role in production control and monitoring.

[0034] However, the current environmental Internet of Things is not systematic enough in production control monitoring, and cannot integrate data resources for reasonable real-time processing of production control. Especially for production processes such as bonding copper wires that require high precision and high quality, it is necessary to reasonably coordinate and efficiently control the entire production cluster equipment to effectively ensure the quality of bonding copper wires.

[0035] refer to Figure 1The embodiment of the present invention provides an intelligent copper bonding wire equipment cluster control method based on the environmental Internet of Things. The method uses the environmental Internet of Things to construct a production monitoring and control system for the copper bonding wire equipment cluster. On the one hand, the control parameters to be monitored are analyzed based on historical big data to determine the reasonable range of the control parameters in the production process. On the other hand, accurate and reasonable parameter comparison basic features are provided for subsequent real-time control monitoring and analysis to ensure that the real-time monitoring and control of the control parameters is accurate and effective, so that the production quality of the copper bonding wire can be effectively guaranteed. In addition, since the environmental Internet of Things is used to collect and analyze data, combined with the environmental Internet of Things, the energy generated by the equipment cluster in the production process can be further reasonably utilized, which adds new elements to the concept of smart factories, further improves the utilization rate of energy in production, and has energy-saving and environmental protection effects. Of course, since the environmental Internet of Things uses a low-power information transmission method, it can effectively transmit and spread information in complex equipment cluster areas without greatly reducing the signal of information transmission due to regional terrain and equipment installation conditions.

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

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

[0038] Acquire historical data of cluster production control, and perform monitoring feature extraction based on monitoring categories to form characteristic data of cluster production control parameters, including: acquire historical data of cluster production control, perform feature extraction based on adjustment control parameters to form characteristic data of regulation parameters; acquire historical data of cluster production control, perform feature extraction based on capacity control parameters to form characteristic data of capacity control parameters.

[0039] In order to extract the characteristic information of control parameters in the production process of bonding copper wire, two types of control parameters are mainly considered. One is the control parameters that determine the production quality of bonding copper wire in the production process, and the other is the control parameters that affect the production capacity of bonding copper wire. Feature extraction of these two types of control parameters can realize in-process quality control in the production process of bonding copper wire, and adjust the parameters in real time to ensure the quality of bonding copper wire. It can also realize control and monitoring of production to avoid large fluctuations in production caused by changes in control parameters and fail to meet production output needs.

[0040] Acquire historical data of cluster production control, perform feature extraction based on adjustment control parameters, and form control parameter feature data, including: taking bonding copper wire performance parameters as a reference, extracting historical control parameters of all adjustment control parameters in production projects with the same bonding copper wire performance parameters in cluster production control historical data; for different adjustment control parameters, perform the following feature extraction processing based on data type according to the corresponding historical control parameters to form corresponding control parameter features: for data collected for adjustment control parameters that are continuous data, determine minimum and maximum values ​​according to the corresponding historical control parameters to form continuous features of control parameters; for data collected for adjustment control parameters that are discrete data, determine minimum, maximum, minimum collection time interval, and maximum collection time interval according to the corresponding historical control parameters to form discrete features of control parameters; aggregate the control parameter features of all adjustment control parameters to form a control parameter feature set, and calibrate the bonding copper wire performance parameters.

[0041] For the extraction of characteristic information of the adjustment control parameters that determine the production quality of the bonding copper wire, the types of parameter data collected corresponding to different adjustment control parameters are considered. After all, different types of data have different aspects and focuses on feature extraction, so as to accurately and reasonably reflect the characteristics of the control parameters. Most of the parameter data corresponding to the adjustment control parameters in the production process of the bonding copper wire are of two categories, one is a continuous data type and the other is a discrete data type. For the continuous data type, the main thing is to obtain the maximum and minimum values ​​that can be determined under the historical data, and then determine a reasonable range. Of course, for historical big data, the maximum and minimum values ​​can also consider the situation of data deviation. Therefore, before determining the maximum and minimum values, preprocessing can be performed to exclude the influence of a few data deviations on value extraction. For discrete data types, although they also have a certain reasonable numerical range, considering that discrete data collection also has certain time characteristics, the time interval between adjacent data collections is also counted for the maximum and minimum values ​​to form an effective and reasonable collection time interval feature. Similarly, for the case of extracting extreme values, it is also necessary to preprocess the initial big data to exclude deviations. In addition, it should be noted that considering the usage scenarios of the bonding copper wire and other situations, the performance parameters of the bonding copper wire determined each time the bonding copper wire is produced may be different. Therefore, the parameter range of the adjustment control parameters obtained should also be analyzed and processed by big data from the same bonding copper wire production project to avoid large deviations in the characteristics obtained due to different performance parameters of the bonding copper wire.

[0042] Acquire historical data of cluster production control, perform feature extraction based on capacity control parameters, and form capacity control parameter feature data, including: taking bonding copper wire performance parameters as a reference, extracting historical capacity parameters of all capacity control parameters in production projects with the same bonding copper wire performance parameters in the historical data of cluster production control; arranging different capacity control parameters in sequence according to production processes to form a production control parameter sequence set; taking production projects as units, determining the effective capacity fluctuation range of each capacity control parameter in the capacity control parameter sequence set in different production projects under the same bonding copper wire performance parameters, and forming a process capacity-project relationship feature corresponding to the capacity control parameter sequence set; for all production projects with the same bonding copper wire performance parameters, performing a combined operation on the effective capacity fluctuation range of the same capacity control parameter in the process capacity-project relationship feature, and forming a process capacity relationship feature corresponding to the bonding copper wire performance parameters.

[0043] For capacity control parameters, on the one hand, the capacity is affected by the process, and on the other hand, it will be different due to the change of the adjustment control parameters. Therefore, when extracting features, the bonding copper wire performance parameters are still used as the classification method to determine more reasonable feature data. Of course, for capacity control parameters, since the final output of bonding copper wire is gradually formed from the raw materials through the sequentially arranged processes, different processes in this process will more or less damage some raw materials. Therefore, when obtaining the characteristics of capacity control parameters, it is done from the entire process. That is, the parameters of capacity control parameters are expressed in the process sequence of capacity transfer to form a capacity chain.

[0044] S2: Collect real-time data of cluster production control, and combine it with the characteristic data of cluster production control to analyze the production status and form real-time monitoring result data of production status.

[0045] Collect cluster production control real-time data, and combine it with cluster production control characteristic data to perform production status analysis to form real-time production status monitoring result data, including: collect real-time control parameters of adjustment control parameters, and combine them with control parameter characteristic data to perform control production status analysis to form control production status real-time monitoring result information; collect real-time capacity control parameters of capacity control parameters, and combine them with capacity control parameter characteristic data to perform capacity production status analysis to form capacity production status real-time monitoring result information.

[0046] In order to determine whether the collected real-time monitoring data is the reasonable parameter data required for control, the gap between the real-time monitoring data and the big data is also compared. Therefore, the use of real-time data and the corresponding feature data for comparative analysis can accurately, reasonably and efficiently complete real-time monitoring, ensuring real-time and effective production control monitoring of cluster equipment.

[0047] Collect the real-time control parameters of the adjustment control parameters, and combine them with the characteristic data of the control parameters to analyze the control production status and form the real-time monitoring result information of the control production status, including: collect the real-time control parameters of the adjustment control parameters, and perform the following real-time self-test analysis on the two real-time control parameters collected from the same adjustment control parameters: if the two real-time control parameters are empty parameters, and other adjustment control parameters have collected valid data, then the control collection fault warning information is formed; if the two real-time control parameters are empty parameters, and other adjustment control parameters have not collected valid data, then the non-working state information is formed; if any one of the two real-time control parameters is an empty parameter, and the other real-time control parameter belongs to the corresponding control parameter If the corresponding control parameter feature in the quantity feature set is matched, a single effective control information is formed, and the real-time control parameter belonging to the corresponding control parameter feature in the corresponding control parameter feature set is output; if any one 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 corresponding control parameter feature set, a double control acquisition fault warning information is formed; if the difference between the two real-time control parameters exceeds the comparison error threshold, a double control acquisition error over-limit warning information is formed; 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 feature in the corresponding control parameter feature set, a double control acquisition normal information is formed, and the average real-time control parameter is output.

[0048] For the real-time data comparison of adjusting control parameters, different from other data acquisition systems, based on the data collected by the environmental Internet of Things, this application considers the rationality of real-time data collection on the one hand, and on the other hand, it also considers that the environmental Internet of Things saves energy and reduces the cost of data collection while collecting data with the help of environmental energy. Whether the collected data is valid and correct can be achieved by collecting the same control parameter at two collection points. Compared with the traditional data acquisition system, there will be no huge cost increase, but it does have a more effective analysis reference in judging the correctness and rationality of real-time data collection. It should be noted that when conducting comparative analysis, it will be different due to the situation of the two real-time data collected. When two real-time parameters are empty parameters, consider the collection of other control parameters. If only the analyzed parameters are empty parameters, it means that an error has occurred in extracting data. If all control parameters are empty parameters, it can be determined that the current cluster equipment has not started working and production. For two collected real-time parameters, if one has parameters and the other does not, it is necessary to compare and analyze the acquired parameters in combination with the features extracted from big data. If the parameters belong to the feature range, it is determined that one of the acquisition points without extracted parameters is faulty and the other is valid. If the collected parameters do not belong to the feature range, it proves that both acquisition points have failed. If the real-time parameters acquired by the two acquisition points have numerical data and the difference between the two parameters is too large, it also proves that the acquisition point is faulty. If the difference between the two parameters is not large and both belong to the corresponding feature range, the average parameter value can be used to refer to the real-time parameters of the corresponding parameters. By comparing the parameters collected by the two acquisition points with each other and combining them with the corresponding feature range, on the one hand, the correctness of the collected data can be further accurately determined, and on the other hand, the collected data can be used to determine whether production is in progress, which expands the content of the collected parameter analysis and judgment, making the analysis more comprehensive and accurate.

[0049] The real-time control parameters belong to the corresponding control parameter features in the corresponding control parameter feature set, and must meet the following conditions: if the real-time control parameters are continuous data, the values ​​of the real-time control parameters belong to the value range of the corresponding continuous features of the control parameters; if the real-time control parameters are discrete data, the real-time control parameters ensure that the values ​​of the real-time control parameters belong to the value range of the corresponding continuous features of the control parameters, and the range of the collection time intervals of the real-time control parameters belongs to the collection time interval range of the corresponding continuous features of the control parameters.

[0050] When judging whether the real-time data acquired for the same control parameter belongs to the corresponding characteristic 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 value, while for discrete data, it is necessary not only to determine that the parameter value is within the range established by the corresponding extreme value, but also to determine that the range of the acquired time interval also belongs to the range established by the corresponding extreme value.

[0051] Collect real-time capacity control parameters of capacity control parameters, and combine with the characteristic data of capacity control parameters to analyze the capacity production status and form real-time monitoring result information of capacity production status, including: determining the real-time capacity control range corresponding to each capacity control parameter according to the collected real-time capacity control parameters of capacity control parameters; arranging the real-time capacity control ranges of different capacity control parameters according to the process sequence to form real-time relationship data of process capacity; determining the process capacity relationship characteristics corresponding to the real-time relationship data of different process capacities according to the performance parameters of bonding copper wire, and combining the real-time monitoring result information of production status regulation to analyze the capacity status and form real-time monitoring result data of capacity production status.

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

[0053] According to 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 performed in combination with the real-time monitoring result information of the control production status to form the real-time monitoring result data of the capacity production status, including: if the different real-time capacity control ranges in the process capacity real-time relationship data all belong to the effective fluctuation range of the corresponding capacity control parameters in the process capacity relationship characteristic data, then normal capacity control information is formed; if the real-time capacity control ranges in the process capacity real-time relationship data all belong to 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 type capacity abnormality warning information is formed; if the real-time capacity control ranges in the process capacity real-time relationship data all belong to the effective fluctuation range of the corresponding capacity control parameters in the process capacity relationship characteristic data, and there are abnormalities in the real-time control parameters under the corresponding process, then control type capacity abnormality warning information is formed.

[0054] When conducting analysis and judgment, there will be different capacity status judgments due to different comparison results between real-time capacity parameters and relationship characteristics. If each real-time capacity parameter belongs to the corresponding capacity characteristic range, the entire production process is stable and the output is stable and reaches the demand. If there are real-time capacity parameters that do not belong to the corresponding capacity characteristic range, and the adjustment control parameters under the corresponding process are determined to be not within the corresponding characteristic range, then it may be due to the abnormal capacity caused by the change of non-adjustment control parameters such as material transfer. If there are real-time capacity parameters that do not belong to the corresponding capacity characteristic range, and the adjustment control parameters under the corresponding process are determined to be not within the corresponding characteristic range, then it may be due to the abnormal adjustment of the control parameters. The abnormal capacity situation. For different types of abnormal judgments, production control adjustments can be made in a targeted manner, which greatly improves the timeliness and accuracy of production control.

[0055] The present invention also provides an intelligent bonding copper wire equipment cluster control system based on environmental Internet of Things, the system comprising: A-IoT acquisition terminals, including A-IoT acquisition terminals of different energy acquisition types, which are arranged on corresponding process equipment to collect different real-time control parameters and real-time production capacity control parameters corresponding to the process, and for A-IoT acquisition terminals that collect different real-time control parameters, the collection of each adjustment control parameter is completed by two identical A-IoT acquisition terminals; an A-IoT data acquisition unit, which is used to collect real-time parameters collected by all A-IoT acquisition terminals; an A-IoT database, which is used to store cluster production control historical data; an A-IoT data processing and feedback unit, which is used to obtain cluster production control historical data from the A-IoT database, perform feature extraction, and form cluster production control parameter feature data, which is used to extract the real-time parameters obtained by the A-IoT data acquisition unit, and perform production status analysis in combination with the cluster production control parameter feature data to form real-time monitoring result data of the production status and issue an early warning.

[0056] The system realizes the purpose of data collection by utilizing the surrounding environment energy by setting up an environmental Internet of Things type collection terminal, fully realizing the utilization of the diffusion energy generated by the production of bonding copper wire, and improving the energy utilization rate in the production process. The data collected at each location point is uploaded through a low-power data transmission method, which is stable and reliable, and can avoid the situation where the signal is poor due to object obstruction. The database completes reasonable historical data storage and provides a data basis for big data analysis, while the processing and feedback unit can timely complete the feature data extraction and real-time data comparative analysis to feedback abnormal situations to achieve effective and accurate control of cluster production, and effectively ensure the quality of bonding copper wire. The entire system closely links data collection to processing and analysis to form a complete system for production control and monitoring, which fully meets the high-precision and high-quality production control requirements of bonding copper wire and provides a data basis for intelligent regulation.

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

[0058] This method uses the environmental Internet of Things to build a production monitoring and control system for the bonding copper wire equipment cluster. On the one hand, the control parameters to be monitored are analyzed based on historical big data to determine the reasonable range of the control parameters in the production process. On the other hand, it also provides accurate and reasonable basic characteristics for parameter comparison for subsequent real-time control monitoring and analysis, ensuring that the real-time monitoring and control of the control parameters is accurate and effective, so that the production quality of the bonding copper wire can be effectively guaranteed. In addition, since the environmental Internet of Things is used to collect and analyze data, combined with the environmental Internet of Things, the energy generated by the equipment cluster in the production process can be further reasonably utilized, adding new elements to the concept of smart factories, further improving the utilization rate of energy in production, and having energy-saving and environmental protection effects. Of course, since the environmental Internet of Things uses low-power information transmission methods, it can effectively transmit and spread information in complex equipment cluster areas without greatly reducing the signal of information transmission due to regional terrain and equipment installation conditions.

[0059] The system realizes the purpose of data collection by utilizing the surrounding environment energy by setting up an environmental Internet of Things type collection terminal, fully realizing the utilization of the diffusion energy generated by the production of bonding copper wire, and improving the energy utilization rate in the production process. The data collected at each location point is uploaded through a low-power data transmission method, which is stable and reliable, and can avoid the situation where the signal is poor due to object obstruction. The database completes reasonable historical data storage and provides a data basis for big data analysis, while the processing and feedback unit can timely complete the feature data extraction and real-time data comparative analysis to feedback abnormal situations to achieve effective and accurate control of cluster production, and effectively ensure the quality of bonding copper wire. The entire system closely links data collection to processing and analysis to form a complete system for production control and monitoring, which fully meets the high-precision and high-quality production control requirements of bonding copper wire and provides a data basis for intelligent regulation.

[0060] In the embodiments of the present application, "indication" may include direct indication and indirect indication, and may also include explicit indication and implicit indication. The information indicated by a certain information is called information to be indicated. In the specific implementation process, there are many ways to indicate the information to be indicated, such as but not limited to, the information to be indicated can be directly indicated, such as the information to be indicated itself or the index of the information to be indicated. The information to be indicated can also be indirectly indicated by indicating other information, wherein there is an association between the other information and the information to be indicated. It is also possible to indicate only a part of the information to be indicated, while the other parts of the information to be indicated are known or agreed in advance. For example, the indication of specific information can also be achieved by means of the arrangement order of each piece of information agreed in advance (for example, specified by the protocol), thereby reducing the indication overhead to a certain extent. At the same time, the common parts of each piece of information can also be identified and indicated uniformly to reduce the indication overhead caused by indicating the same information separately.

[0061] In addition, the specific indication method may also be various existing indication methods, such as but not limited to the above-mentioned indication methods and various combinations thereof. The specific details of the various indication methods can refer to the prior art and will not be repeated herein. As can be seen from the above, for example, when it is necessary to indicate multiple information of the same type, different indication methods may be used for different information. In the specific implementation process, the desired indication method can be selected according to specific needs. The embodiment of the present application does not limit the selected indication method. In this way, the indication method involved in the embodiment of the present application should be understood to cover various methods that can 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 and sent separately, and the sending period and / or sending time of these sub-information can be the same or different. The specific sending method is not limited in the embodiment of the present application. Among them, the sending period and / or sending time of these sub-information can be pre-defined, for example, pre-defined according to a protocol, or can be configured by the sending end device by sending configuration information to the receiving end device.

[0063] "Pre-definition" or "pre-configuration" can be implemented by pre-saving corresponding codes, tables or other methods that can be used to indicate relevant information in the device, and the embodiments of the present application do not limit the specific implementation method. Among them, "saving" can mean saving in one or more memories. The one or more memories can be set separately or integrated in an encoder or decoder, a processor, or a communication device. The one or more memories can also be partially set separately and partially integrated in a decoder, a processor, or a communication device. The type of memory can be any form of storage medium, which is not limited by the embodiments of the present application.

[0064] The "protocol" involved in the embodiments of the present application may refer to a protocol family in the communication field, a standard protocol with a similar protocol family frame structure, or a related protocol used in future communication systems, and the embodiments of the present application do not make specific limitations on this.

[0065] In the embodiments of the present application, descriptions such as "when...", "in the case of...", "if" and "if" all mean that the device will make corresponding processing under certain objective circumstances. It does not limit the time, nor does it require the device to have a judgment action when implementing it, nor does it mean that there are other limitations.

[0066] In the description of the embodiments of the present application, unless otherwise specified, " / " indicates that the objects associated before and after are in an "or" relationship, for example, A / B can represent A or B; "and / or" in the embodiments of the present application is only a description of the association relationship of the associated objects, indicating that there can be three relationships, for example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. In addition, in the description of the embodiments of the present application, unless otherwise specified, "multiple" refers to two or more than two. "At least one of the following" or its similar expression refers to any combination of these items, including any combination of single items 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, c can be single or multiple. In addition, in order to facilitate the clear description of the technical solution of the embodiments of the present application, in the embodiments of the present application, the words "first" and "second" are used to distinguish the same or similar items with basically the same functions and effects. Those skilled in the art will appreciate that words such as "first" and "second" do not limit the quantity and execution order, and words such as "first" and "second" do not necessarily limit the difference. At the same time, in the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a concrete way for easy understanding.

[0067] It should be understood that the processor in the embodiment of the present application may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

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

[0069] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of 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, the process or function described in the embodiment of the present application is generated in whole or in part. 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 computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). 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 contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0070] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific 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 refers 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 be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0072] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean 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 the present application.

[0073] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example 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 performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

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

[0075] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

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

[0077] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0078] If the functions are implemented in the form of 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 the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0079] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for controlling a cluster of intelligent copper bonding wire equipment based on environmental Internet of Things, characterized in that: include: Obtain cluster production control historical data, and extract monitoring features based on monitoring categories to form cluster production control parameter feature data; Collect real-time data of cluster production control, and combine it with the characteristic data of cluster production control to analyze the production status and form real-time monitoring result data of production status.

2. The intelligent copper bonding wire equipment cluster control method based on environmental Internet of Things according to claim 1 is characterized in that: The acquisition of cluster production control historical data and the extraction of monitoring features based on monitoring categories to form cluster production control parameter feature data include: Acquire the cluster production control historical data, perform feature extraction based on adjustment control parameters, and form control parameter feature data; The cluster production control historical data is obtained, and feature extraction based on capacity control parameters is performed to form capacity control parameter feature data.

3. The intelligent copper bonding wire equipment cluster control method based on environmental Internet of Things according to claim 2 is characterized in that: The step of obtaining the cluster production control historical data and extracting features based on adjustment control parameters to form control parameter feature data includes: Taking the performance parameters of the bonding copper wire as a reference, extracting the historical control parameters of all adjustment control parameters in the production projects with the same performance parameters of the bonding copper wire in the cluster production control historical data; For different adjustment control parameters, the following feature extraction processing based on the data type is performed according to the corresponding historical control parameters to form corresponding control parameter features: The data collected for adjusting the control parameters are continuous data, and the minimum and maximum values ​​are determined according to the corresponding historical control parameters to form the continuous characteristics of the control parameters; The data collected for adjusting control parameters are discrete data. According to the corresponding historical control parameters, the minimum value, maximum value, minimum collection time interval and maximum collection time interval are determined to form discrete characteristics of the control parameters; The control parameter characteristics of all adjustment control parameters are collected to form a control parameter characteristic set, and the bonding copper wire performance parameters are calibrated.

4. The intelligent copper bonding wire equipment cluster control method based on environmental Internet of Things according to claim 3 is characterized in that: The acquiring of the cluster production control historical data, performing feature extraction based on capacity control parameters, and forming capacity control parameter feature data includes: Taking the performance parameters of the bonding copper wire as a reference, extracting the historical capacity parameters of all capacity control parameters in the production projects with the same performance parameters of the bonding copper wire in the cluster production control historical data; Arrange different capacity control parameters in sequence according to the production process to form a production control parameter sequence set; Taking production projects as units, determine the effective capacity fluctuation range of each capacity control parameter in the capacity control parameter sequence set in different production projects under the same bonding copper wire performance parameters, and form the process capacity project relationship characteristics corresponding to the capacity control parameter sequence set; For all production projects with the same bonding copper wire performance parameters, the effective capacity fluctuation range of the same capacity control parameter in the process capacity project relationship characteristics is combined and calculated to form the process capacity relationship characteristics corresponding to the bonding copper wire performance parameters.

5. The intelligent copper bonding wire equipment cluster control method based on environmental Internet of Things according to claim 4 is characterized in that: The real-time data of cluster production control is collected, and combined with the characteristic data of cluster production control, production status analysis is performed to form real-time monitoring result data of production status, including: Collect and adjust the real-time control parameters of control parameters, and analyze the control production status in combination with the characteristic data of the control parameters to form real-time monitoring result information of the control production status; Collect real-time capacity control parameters of capacity control parameters, and combine them with characteristic data of the capacity control parameters to analyze the capacity production status and form real-time monitoring result information of the capacity production status.

6. The intelligent copper bonding wire equipment cluster control method based on environmental Internet of Things according to claim 5 is 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 the real-time monitoring result information of the control production status, including: Collect the real-time control parameters of the adjustment control parameters, and perform the following real-time self-check analysis on the two real-time control parameters collected from the same adjustment control parameters: If the real-time control parameters in the two places are empty parameters, and other adjustment control parameters have collected valid data, then a control collection failure warning information is generated; If the real-time control parameters in the two places are empty parameters, and no valid data is collected for other adjustment control parameters, non-working state information is generated; If any one of the two real-time control parameters is a null parameter, and the other real-time control parameter belongs to the corresponding control parameter feature in the corresponding control parameter feature set, a single valid control information is formed, and the real-time control parameter belonging to the corresponding control parameter feature in the corresponding control parameter feature set is output; If any one of the two real-time control parameters is a null parameter, and the other real-time control parameter does not belong to the corresponding control parameter feature in the corresponding control parameter feature set, then a double control acquisition fault warning information is generated; If the difference between the two real-time control parameters exceeds the comparison error threshold, a double control acquisition error exceeding limit warning information is 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 corresponding control parameter feature set, normal information of dual control collection is formed, and the average real-time control parameter is output.

7. The intelligent copper bonding wire equipment cluster control method based on environmental Internet of Things according to claim 6 is characterized in that: The real-time control parameter belongs to the corresponding control parameter feature in the corresponding control parameter feature set and must meet the following conditions: If the real-time control parameter is continuous data, the value of the real-time control parameter belongs to the value range of the corresponding continuous characteristic of the control parameter; If the real-time control parameter is discrete data, the real-time control parameter ensures that the value of the real-time control parameter belongs to the value range of the corresponding continuous characteristic of the control parameter, and the range of the collection time interval of the real-time control parameter belongs to the collection time interval range of the corresponding continuous characteristic of the control parameter.

8. The intelligent copper bonding wire equipment cluster control method based on environmental Internet of Things according to claim 7 is characterized in that: The real-time capacity control parameters of the 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 result information of the capacity production status, including: Determine the real-time capacity control range corresponding to each capacity control parameter according to the real-time capacity control parameters of the collected capacity control parameters; Arranging the real-time capacity control ranges of different capacity control parameters according to the process sequence to form real-time relationship data of process capacity; According to 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, and the capacity status analysis is performed in combination with the real-time monitoring result information of the production status regulation to form the real-time monitoring result data of the capacity production status.

9. The intelligent copper bonding wire equipment cluster control method based on environmental Internet of Things according to claim 8 is characterized in that: The process capacity relationship characteristics corresponding to the different process capacity real-time relationship data are determined according to the bonding copper wire performance parameters, and the capacity status analysis is performed in combination with the real-time monitoring result information of the production status regulation to form the capacity production status real-time monitoring result data, including: If the different real-time capacity control ranges in the process capacity real-time relationship data all belong to the effective fluctuation range of the corresponding capacity control parameters in the process capacity relationship characteristic data, normal capacity control information is generated; If the real-time capacity control range in the process capacity real-time relationship data belongs to the effective fluctuation range of the corresponding capacity control parameters in the process capacity relationship characteristic data, and all the real-time control parameters under the corresponding process are normal, then a non-control capacity abnormality warning information is generated; If the real-time capacity control range in the process capacity real-time relationship data belongs to the effective fluctuation range of the corresponding capacity control parameters in the process capacity relationship characteristic data, and there is an abnormality in the real-time control parameters under the corresponding process, then a control type capacity abnormality warning information is generated.

10. An intelligent copper bonding wire equipment cluster control system based on environmental Internet of Things, adopting the intelligent copper bonding wire equipment cluster control method based on environmental Internet of Things according to any one of claims 1 to 9, characterized in that: include: A-IoT acquisition terminals, including A-IoT acquisition terminals of different energy acquisition types, are arranged on corresponding process equipment to collect different real-time control parameters and real-time capacity control parameters corresponding to the process, and for the A-IoT acquisition terminals that collect different real-time control parameters, the collection 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 collected by all the A-IoT acquisition terminals; A-IoT database, used to store cluster production control historical data; The A-IoT data processing and feedback unit is used to obtain the cluster production control historical data from the A-IoT database, perform feature extraction, and form cluster production control parameter feature data, which is used to extract the real-time parameters obtained by the A-IoT data acquisition unit, and perform production status analysis in combination with the cluster production control parameter feature data to form real-time monitoring result data of the production status and issue an early warning.

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