Selective control method and system for intelligent bonding silver wire equipment based on big data

Abstract

The invention relates to the technical field of intelligent manufacturing, in particular to an intelligent bonding silver wire equipment selective control method and system based on big data, and the method comprises the following steps: collecting real-time data, including temperature reading, pressure value and mechanical operation speed, of bonding silver wire equipment, carrying out the data updating and synchronization, and generating an equipment real-time data set. According to the method, by selectively adjusting the control parameters, the product quality is optimized, the production cost is remarkably reduced, testing and evaluation of differential control setting are simulated, the adaptability of the adjustment strategy of the bonding silver wire equipment is enhanced, the equipment operation efficiency is improved, the control scheme is updated and optimized in real time, and key performance indexes are continuously monitored; according to the method, the sustainability and the high qualified rate of the production process are ensured, meanwhile, the market competitiveness is improved, the performance fluctuation occurring under single setting is effectively made up through dynamic adjustment and real-time optimization of production parameters, and the consistency of product quality and the stability of the production process are ensured.

Description

Technical Field

[0001] The present invention relates to the field of intelligent manufacturing technology, and in particular to a method and system for selectively controlling intelligent silver wire bonding equipment based on big data. Background Art

[0002] The field of intelligent manufacturing technology covers strategies and methods that use computer control and big data analysis to improve the automation and efficiency of manufacturing processes. This field focuses on optimizing production processes and optimizing resource allocation by integrating advanced information technology and industrial automation. The core of intelligent manufacturing includes technologies such as the Internet of Things, artificial intelligence, machine learning, and big data analysis, which work together in manufacturing systems to improve production efficiency, reduce resource waste, and improve product quality. Intelligent manufacturing also emphasizes real-time data collection and analysis during the production process to support decision-making and continuous improvement, and is one of the key technologies driving the development of Industry 4.0.

[0003] Among them, the big data intelligent silver wire bonding equipment selective control method refers to the use of big data technology to analyze and guide the operation of silver wire bonding equipment in semiconductor manufacturing to achieve more accurate and effective production control. This method collects equipment operation data, production process parameters and quality control results, and uses data analysis algorithms to find the best operation mode and predict potential production problems. The main purpose is to improve the degree of automation of the production line, reduce human errors, optimize product quality, reduce production costs and improve market competitiveness. The application of this technology not only enhances the intelligence and flexibility of the production process, but also helps companies quickly respond to market changes and customer needs to achieve efficient and sustainable production.

[0004] Although information technology and industrial automation are integrated in the existing technology, there are limitations in real-time data analysis and instant feedback adjustment of the production process. Conventional production control methods rely on preset parameters and relatively fixed operating modes, and it is difficult to flexibly respond to immediate changes on the production line. This lack of real-time analysis and dynamic adjustment capabilities leads to limited production efficiency and equipment response speed, affecting the overall performance of the production line and product quality. For example, in semiconductor manufacturing, any slight deviation will lead to an increase in the scrap rate of the entire batch of products, and the shortcomings of the existing technology in quickly identifying and adjusting these deviations lead to waste of resources and rising costs. The lack of the ability to optimize and predict potential problems in real time also limits the flexibility of the production process and the ability of enterprises to respond quickly to market changes. Summary of the invention

[0005] In order to solve the technical problem that the existing technology integrates information technology and industrial automation, but has limitations in real-time data analysis and immediate adjustment of the production process. Conventional production control methods rely on preset parameters and relatively fixed operating modes, and it is difficult to flexibly respond to immediate changes on the production line. This lack of real-time analysis and dynamic adjustment capabilities leads to limited production efficiency and equipment response speed, affecting the overall performance of the production line and product quality. For example, in semiconductor manufacturing, any slight deviation will lead to an increase in the scrap rate of the entire batch of products, and the shortcomings of the existing technology in quickly identifying and adjusting deviations lead to waste of resources and rising costs. The lack of the ability to optimize and predict potential problems in real time also limits the flexibility of the production process and the ability of enterprises to respond quickly to market changes. An embodiment of the present invention provides a selective control method and system for intelligent bonding silver wire equipment based on big data. The technical solution is as follows:

[0006] On the one hand, a method for selectively controlling an intelligent silver wire bonding device based on big data is provided, the method comprising:

[0007] S1: Collect real-time data of silver wire bonding equipment, including temperature readings, pressure values, and mechanical operating speed, and update and synchronize data to generate real-time data sets of equipment;

[0008] S2: Through the real-time data set of the equipment, using big data, extracting normal production data, analyzing deviations in temperature, pressure and speed, automatically identifying abnormal data in the production process, and obtaining production data analysis results;

[0009] S3: According to the production data analysis results, selectively adjust the control parameters of the silver bonding wire equipment, adjust the temperature and pressure, set a new speed range of the silver bonding wire equipment, match the production standard, and generate a parameter adjustment record;

[0010] S4: Based on the parameter adjustment record, simulate the impact of differentiated control settings on product quality, evaluate the impact and effect of multiple operation options by testing differentiated control settings, and obtain simulation prediction results;

[0011] S5: using the simulation prediction results, adjusting the settings of the silver wire bonding equipment through selective control, updating the production parameters in real time to optimize the bonding process, and generating an optimized control plan;

[0012] S6: Implement the optimization control scheme, continuously monitor the key performance indicators of the silver wire bonding equipment, including the operating efficiency of the silver wire bonding equipment and the product qualification rate, and evaluate the performance difference of the silver wire bonding equipment before and after the improvement to obtain the control effect evaluation results.

[0013] As a further solution of the present invention, the real-time data set of the equipment includes temperature readings, pressure values, and mechanical operating speeds of the bonding silver wire equipment; the production data analysis results include temperature standard deviations, pressure anomalies, and speed fluctuation data in the production process; the parameter adjustment records include temperature adjustment values, pressure adjustment ranges, and set speed update parameters; the simulation prediction results include data on the impact of differentiated control settings on product quality and comparison of the effects of operation selections; the optimization control scheme includes real-time optimization of control parameters, adjustment schemes for equipment settings, and update records of production parameters; the control effect evaluation results include improvement data on the operating efficiency of the bonding silver wire equipment, optimization records of product qualification rates, and evaluation and analysis of performance improvements.

[0014] As a further solution of the present invention, the real-time data of the silver wire bonding device, including temperature readings, pressure values ​​and mechanical running speed, is collected, and the data is updated and synchronized. The steps of generating the real-time data set of the device are specifically as follows:

[0015] S101: collecting real-time data of the silver wire bonding device, installing a temperature sensor on a heating part of the silver wire bonding device, fixing a pressure sensor on a pressing part of the silver wire bonding device, setting the sensor to upload data at a fixed time, including temperature readings and pressure values, to generate a real-time monitoring data set;

[0016] S102: Using the real-time monitoring data set, identifying abnormal points in the real-time monitoring data by setting a safe operating temperature range and a pressure threshold, and obtaining an updated data set if the temperature exceeds a preset peak value and the pressure is lower than a low safety value;

[0017] S103: using the update data set, broadcasting the update data to the terminal devices connected to the network, checking the real-time reception of the current operation status data, and generating a real-time data set for the device.

[0018] As a further solution of the present invention, the steps of extracting normal production data, analyzing temperature, pressure and speed deviations, automatically identifying abnormal data in the production process, and obtaining production data analysis results by using the real-time data set of the equipment and big data are specifically as follows:

[0019] S201: Using the real-time data set of the equipment and big data, filter the production data marked as normal, including normal value interval data of temperature, pressure and speed, to obtain a standard production data set;

[0020] S202: Based on the standard production data set, compare the deviations of the data points in the equipment real-time data set with the standard production data, set the deviation threshold, identify the abnormal points of temperature, pressure and speed, and generate abnormal data identification results;

[0021] S203: Utilize the abnormal data identification result to statistically analyze the frequency and distribution of abnormal data in the entire production process, analyze the time point of abnormality occurrence and production rhythm in combination with the timestamp, and optimize the production process to obtain the production data analysis result.

[0022] As a further solution of the present invention, according to the production data analysis results, the control parameters of the silver wire bonding equipment are selectively adjusted, the temperature and pressure are adjusted, a new range of the speed of the silver wire bonding equipment is set, and the production standard is matched. The steps of generating the parameter adjustment record are specifically as follows:

[0023] S301: using the production data analysis results, identifying the control parameters that need to be adjusted, adjusting the abnormal temperature and pressure points, updating the current temperature upper limit and pressure lower limit, and generating a temperature and pressure adjustment plan;

[0024] S302: Based on the temperature and pressure adjustment scheme, a response surface method is used to set a speed range of the silver wire bonding device, verify the matching degree between the speed setting and the adjusted temperature and pressure parameters, and generate an optimal speed adjustment scheme;

[0025] S303: According to the optimal speed adjustment scheme and in combination with the temperature and pressure adjustment scheme, the silver wire bonding equipment is selectively controlled, the operation interface and control logic of the silver wire bonding equipment are updated, and the changed parameters and execution time are recorded to generate a parameter adjustment record.

[0026] As a further solution of the present invention, the formula of the response surface method is as follows:

[0027]

[0028] Among them, S is the speed adjustment value, T represents the temperature value, P represents the pressure value, V represents the original equipment speed value, K T , K P and K V Represent the adjustment coefficients for temperature, pressure and speed respectively.

[0029] As a further solution of the present invention, based on the parameter adjustment record, the influence of the differentiated control settings on the product quality is simulated, and the influence and effect of various operation options are evaluated by testing the differentiated control settings to obtain the simulation prediction results. Specifically, the steps are as follows:

[0030] S401: using the parameter adjustment record, setting a virtual simulation environment to simulate actual production conditions, and applying a variety of differentiated control settings, including differentiated combinations of temperature, pressure, and speed, to generate a simulation test configuration;

[0031] S402: Based on the simulation test configuration, run a simulation test, collect product quality data under each control setting, including structural integrity, durability, and a ratio that meets performance indicators, and obtain simulation test data;

[0032] S403: Using the simulation test data, evaluate the impact of various control parameter settings on product quality, determine the optimal equipment control strategy by comparing the test results with the degree of compliance with production standards, and generate simulation prediction results.

[0033] As a further solution of the present invention, the simulation prediction results are used to selectively control and adjust the settings of the silver wire bonding equipment, and the production parameters are updated in real time to optimize the bonding process. The steps of generating the optimization control solution are specifically as follows:

[0034] S501: identifying the optimal temperature, pressure and speed settings according to the simulation prediction results, adjusting the parameters of the silver wire bonding equipment in the production environment, and generating a real-time parameter adjustment plan;

[0035] S502: Based on the real-time parameter adjustment scheme, the device is programmed through the control software to automatically adjust the temperature, pressure and operating speed of the device, verify that each parameter can achieve the predetermined optimal effect, and generate a control parameter implementation record;

[0036] S503: Apply the adjustment content in the control parameter implementation record to the production line, monitor the adjusted equipment operation status and production quality, update the production parameters in real time to optimize the entire bonding process, and generate an optimized control solution.

[0037] As a further solution of the present invention, the optimization control scheme is implemented to continuously monitor the key performance indicators of the silver bonding wire equipment, including the operation efficiency of the silver bonding wire equipment and the product qualification rate, and the performance difference of the silver bonding wire equipment before and after the improvement is evaluated. The steps of obtaining the control effect evaluation result are specifically as follows:

[0038] S601: Implement the optimization control scheme, continuously monitor the operating efficiency and product qualification rate of the silver wire bonding equipment, and generate a performance monitoring data set;

[0039] S602: using the performance monitoring data set, using principal component analysis technology to compare the operating efficiency and product qualification rate before and after optimization, identifying key factors for performance optimization, and obtaining performance comparison analysis results;

[0040] The formula for the principal component analysis technique is as follows:

[0041]

[0042] Where E is the performance evaluation index, EF represents the optimization rate of operating efficiency, PR represents the optimization rate of product qualification rate, Var(D) represents the variance of monitoring parameter variation in the data set, IR represents the risk ratio in performance optimization, and ω1, ω2, ω3, and ω4 are weight coefficients.

[0043] S603: According to the performance comparison and analysis results, the overall control strategy effect is evaluated, the performance optimization of the adjusted control scheme is identified, and a control effect evaluation result is generated.

[0044] On the other hand, an electric vehicle state monitoring system is provided, wherein the electric vehicle state monitoring system is used to execute the above electric vehicle state monitoring method, and the system comprises:

[0045] The data analysis module collects the temperature readings, pressure values ​​and mechanical running speed of the silver wire bonding equipment, updates the data synchronously, and generates a real-time data set;

[0046] The abnormal data identification module screens the temperature, pressure and speed data that are deviated from the conventional production data based on the real-time data set, records the deviated data points, and marks them as abnormal to obtain an abnormal marking result;

[0047] The parameter optimization module adjusts the control parameters of temperature and pressure according to the abnormal marking result, sets the speed range of the silver wire bonding equipment, matches the production standard, and obtains the adjustment parameter record;

[0048] The scenario simulation module uses the adjustment parameter records and differentiated control strategies to perform multi-scenario simulations, evaluate product quality under differentiated scenarios, and generate simulation prediction results;

[0049] The strategy execution module uses the simulation prediction results to optimize the control settings of the silver wire bonding equipment, update the production control parameters in real time, and monitor key performance indicators, including equipment operating efficiency and product qualification rate, continuously evaluate the improvement effect, and obtain the control effect evaluation results.

[0050] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0051] By collecting the operation data of the silver bonding wire equipment in real time and updating it synchronously, the big data analysis technology is used to automatically identify anomalies in the production process, effectively improving the accuracy and response speed of production control. By analyzing the production data and selectively adjusting the control parameters, not only the product quality is optimized, but also the production cost is significantly reduced. Simulate the test and evaluation of differentiated control settings to enhance the adaptability of the adjustment strategy of the silver bonding wire equipment and improve the equipment operation efficiency. Real-time update optimization control scheme and continuous monitoring of key performance indicators ensure the sustainability and high pass rate of the production process, while improving market competitiveness. Dynamic adjustment and real-time optimization of production parameters effectively compensate for the performance fluctuations under a single setting, ensuring the consistency of product quality and the stability of the production process. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0053] Figure 2 This is a detailed flow chart of S1 of the present invention;

[0054] Figure 3 This is a detailed flow chart of S2 of the present invention;

[0055] Figure 4 This is a detailed flow chart of S3 of the present invention;

[0056] Figure 5 This is a detailed flow chart of S4 of the present invention;

[0057] Figure 6 This is a detailed flow chart of S5 of the present invention;

[0058] Figure 7 This is a detailed flow chart of S6 of the present invention;

[0059] Figure 8 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0060] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0061] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0062] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0063] See also Figure 1 The embodiment of the present invention provides a method for selectively controlling an intelligent silver wire bonding device based on big data. The processing flow of the method may include the following steps:

[0064] S1: Collect real-time data of silver wire bonding equipment, including temperature readings, pressure values, and mechanical operating speed, and update and synchronize data to generate real-time data sets of equipment;

[0065] S2: Through the equipment real-time data set, use big data to extract normal production data, analyze the deviation of temperature, pressure and speed between the equipment real-time data and normal production data, automatically identify abnormal data in the production process, and obtain production data analysis results;

[0066] S3: According to the analysis results of production data, selectively adjust the control parameters of the silver bonding wire equipment, adjust the temperature and pressure, set a new range of the speed of the silver bonding wire equipment, match the production standards, and generate parameter adjustment records;

[0067] S4: Based on the parameter adjustment records, simulate the impact of differentiated control settings on product quality. By testing differentiated control settings, evaluate the impact and effect of various operation options and obtain simulation prediction results;

[0068] S5: Using the simulation prediction results, adjust the settings of the silver wire bonding equipment through selective control, update the production parameters in real time to optimize the bonding process, and generate an optimized control plan;

[0069] S6: Implement the optimized control plan, continuously monitor the key performance indicators of the silver wire bonding equipment, including the operating efficiency of the silver wire bonding equipment and the product qualification rate, and evaluate the performance difference before and after the improvement of the silver wire bonding equipment to obtain the control effect evaluation results.

[0070] The real-time data set of the equipment includes the temperature readings, pressure values, and mechanical operating speeds of the silver wire bonding equipment. The production data analysis results include the temperature standard deviation, pressure anomaly points, and speed fluctuation data in the production process. The parameter adjustment records include the temperature adjustment value, pressure adjustment range, and set speed update parameters. The simulation prediction results include the impact data of differentiated control settings on product quality and the effect comparison of operation selection. The optimized control plan includes real-time optimization of control parameters, adjustment plan of equipment settings, and update records of production parameters. The control effect evaluation results include the improvement data of the operating efficiency of the silver wire bonding equipment, optimization records of product qualification rate, and evaluation and analysis of performance improvement.

[0071] See also Figure 2, collect real-time data of the silver bonding wire equipment, including temperature readings, pressure values, and mechanical operating speed, and update and synchronize the data. The steps to generate the real-time data set of the equipment are as follows:

[0072] S101: collect real-time data of the silver wire bonding device, install a temperature sensor on the heating part of the silver wire bonding device, fix a pressure sensor on the pressing part of the silver wire bonding device, set the sensor to upload data at a fixed time, including temperature readings and pressure values, and generate a real-time monitoring data set. The execution process is as follows;

[0073] Install the temperature sensor on the heating part of the silver wire bonding equipment. This operation involves selecting the sensor model to ensure that it can withstand the highest temperature during the operation of the equipment and accurately reflect the temperature changes. The pressure sensor is fixed on the pressing part of the silver wire bonding equipment. The pressure range and accuracy must be considered in the sensor selection process to ensure that the slight changes in the equipment pressure can be accurately captured. The setting of the sensor to upload data at fixed times must consider the real-time data transmission and the stability of the network to ensure that the temperature readings and pressure values ​​can be accurately and timely uploaded to the monitoring system. The successful execution of this process is the key to ensuring the normal operation and maintenance safety of the equipment and generating a real-time monitoring data set.

[0074] S102: Using the real-time monitoring data set, the abnormal points in the real-time monitoring data are identified by setting the safe operating temperature range and pressure threshold. If the temperature exceeds the preset peak value and the pressure is lower than the valley safety value, the execution process of obtaining the updated data set is as follows;

[0075] By setting the safe operating temperature range and pressure threshold to identify abnormal points in real-time monitoring data, according to the formula:

[0076] T actual >T max

[0077] and

[0078] P actual <P min

[0079] Calculate the abnormal data points, where T actual Represents the actual measured temperature, P actual Represents the actual measured pressure, T max and P min Represent the safety thresholds of temperature and pressure respectively;

[0080] If the actual temperature T actual =102℃, pressure P actual =25kPa;

[0081] The safety threshold temperature T max =100℃, pressure Pmin =30kPa;

[0082] Then T actual >T max The condition is met, P actual <P min Also established;

[0083] Therefore, this data point is judged to be abnormal, which indicates that the equipment needs to be checked or adjusted.

[0084] S103: using the updated data set, broadcasting the updated data to the terminal devices connected to the network, checking the real-time receiving current operation status data, and generating the execution process of the device real-time data set is as follows;

[0085] The updated data is broadcast to the terminal devices connected to the network. The terminal devices must have the ability to receive and process real-time data, including data parsing, caching and display. The data broadcasting process involves data encoding, transmission and decoding technology to ensure the security and integrity of the data during transmission. At the same time, the network bandwidth and latency are considered to optimize the data transmission path. The software and hardware configuration of the receiving end must also be adapted to the data processing requirements. The processing of details directly affects whether the operation and maintenance personnel can obtain the operating status of the equipment in a timely and accurate manner, make corresponding adjustments or repairs, and generate real-time data sets for the equipment.

[0086] See also Figure 3 , through the real-time data set of the equipment, using big data, extracting normal production data, analyzing the deviation of temperature, pressure and speed, automatically identifying abnormal data in the production process, and obtaining the production data analysis results in the following steps:

[0087] S201: Using the equipment real-time data set and big data, filter the production data marked as normal, including the normal value interval data of temperature, pressure and speed, and obtain the execution process of the standard production data set as follows;

[0088] Using big data technology to filter out production data marked as normal involves determining the normal value ranges of temperature, pressure and speed. The ranges are based on data analysis. The normal value range setting for each parameter must reflect the typical operating conditions of the equipment and fully consider the design tolerance and safety standards of the equipment. The screening process requires efficient data processing algorithms to process batches of input data to ensure that the data set only contains data points that reflect the normal operating status of the equipment and obtain a standard production data set.

[0089] S202: Based on the standard production data set, compare the deviation of the data points in the equipment real-time data set with the standard production data, set the deviation threshold, identify the abnormal points of temperature, pressure and speed, and generate the abnormal data identification result. The execution process is as follows;

[0090] Compare the deviation between the data points in the equipment real-time data set and the standard production data, according to the formula:

[0091]

[0092] Calculate the deviation value, where T real , P real and S real Represent the actual measured temperature, pressure and speed, T std , P std and S std is the corresponding standard value;

[0093] Set the actual data to T real =100℃,P real =30kPa, S real =1500rpm;

[0094] Standard data is T std =95℃,P std =35kPa, S std =1400rpm;

[0095] The deviation is calculated as

[0096] This result indicates that the data point deviates significantly from the standard production data and should be considered an outlier.

[0097] S203: Utilize the abnormal data identification results to statistically analyze the frequency and distribution of abnormal data in the entire production process, analyze the time point of abnormality occurrence and production rhythm in combination with the timestamp, and optimize the production process. The execution process of obtaining the production data analysis results is as follows;

[0098] Statistical analysis of the frequency and distribution of abnormal data throughout the production process, including recording and analyzing the specific timestamps of each abnormal point to determine the specific time point when the abnormality occurred and its relationship with the production rhythm. The analysis results can reveal potential problem areas in the production process, such as equipment wear, operating errors or fluctuations in raw material quality. Through data analysis, the production process can be further optimized, such as adjusting equipment parameters, optimizing operating steps or redesigning workflows to reduce the occurrence of abnormalities and improve production efficiency, which directly affects the adjustment and improvement strategies of the production line, ensures product quality and production efficiency, and obtains production data analysis results.

[0099] See also Figure 4 According to the analysis results of production data, selectively adjust the control parameters of the silver bonding wire equipment, adjust the temperature and pressure, set a new range of the speed of the silver bonding wire equipment, match the production standards, and generate the parameter adjustment record. The specific steps are:

[0100] S301: Using the production data analysis results, identify the control parameters that need to be adjusted, adjust the abnormal temperature and pressure points, update the current temperature upper limit and pressure lower limit, and generate the temperature and pressure adjustment plan. The execution process is as follows;

[0101] Identify abnormal temperature and pressure points, conduct specific investigations on abnormal temperature and pressure points discovered through data monitoring, evaluate the potential impact on overall production efficiency, and adjust control parameters to correct abnormal data points. Ensure that the production process can run smoothly after the control parameters are adjusted, and update the upper temperature limit and lower pressure limit of the production line. The adjusted temperature and pressure parameters will directly affect the stability of the production process and product quality, ensure the continuous improvement of production efficiency and product quality, and generate a temperature and pressure adjustment plan.

[0102] S302: Based on the temperature and pressure adjustment plan, the response surface method is used to set the speed range of the silver wire bonding equipment, verify the matching degree between the speed setting and the adjusted temperature and pressure parameters, and generate the execution process of the optimal speed adjustment plan as follows;

[0103] The response surface methodology formula is as follows:

[0104]

[0105] Among them, S is the speed adjustment value, T represents the temperature value, P represents the pressure value, V represents the original equipment speed value, K T , K P and K V Represent the adjustment coefficients for temperature, pressure and speed respectively.

[0106] Parameter meaning and calculation process:

[0107] The values ​​of temperature T and pressure P are obtained from the sensor data in the actual equipment operation environment. The current temperature T is set to 300 units (the temperature unit is Celsius, but it is marked with units here), and the pressure P is set to 5 units (according to the on-site monitoring results, the unit is bar or Pascal);

[0108] The original equipment speed V was set to 900 units (in meters per hour) based on the operating manual provided by the equipment manufacturer and actual operation records;

[0109] Weight coefficient K T and K P According to the previous equipment performance test results, K T is 0.5, which means that for every 1 unit increase in temperature, the speed needs to be adjusted by 0.5 units to maintain the performance of the device; K P Set to 0.2, reflecting that the sensitivity of pressure to velocity is lower than that of temperature, K VSet to 1 as the basic adjustment coefficient for speed adjustment;

[0110] Substitute the above parameters into the formula for calculation:

[0111]

[0112] The results show that in order to match the adjusted temperature and pressure parameters, the adjusted speed of the equipment should be set to 14.33 units, which means that the original speed needs to be reduced to adapt to the temperature increase and pressure changes to avoid too fast or too slow speed affecting product quality. In this way, the equipment can maintain the best operating state in a changing production environment to ensure product quality.

[0113] S303: According to the optimal speed adjustment scheme, combined with the temperature and pressure adjustment scheme, the silver wire bonding equipment is selectively controlled, the operation interface and control logic of the silver wire bonding equipment are updated, and the changed parameters and execution time are recorded. The execution process of generating the parameter adjustment record is as follows;

[0114] Update the operating interface and control logic of the silver wire bonding equipment. The operating interface update includes interface layout optimization and adding new control parameter displays. The control logic update involves algorithm adjustment to better respond to the real-time data of the equipment. At the same time, the changed parameters and execution time are recorded to ensure that all adjustments are based on evidence and can be tracked. The changes not only improve the operator's convenience, but also ensure the stability of equipment operation and the consistency of product quality. As an important basis for production process control and subsequent audits, it helps to continuously improve and finely manage the production process and generate parameter adjustment records.

[0115] See also Figure 5 Based on the parameter adjustment records, simulate the impact of differentiated control settings on product quality. By testing differentiated control settings, evaluate the impact and effect of various operation options, and obtain the simulation prediction results in the following steps:

[0116] S401: Using the parameter adjustment record, setting up a virtual simulation environment to simulate actual production conditions, and applying a variety of differentiated control settings, including differentiated combinations of temperature, pressure, and speed, the execution process of generating a simulation test configuration is as follows;

[0117] Setting up a virtual simulation environment to simulate actual production conditions involves building a detailed virtual model that accurately reflects the behavior of the equipment under different temperature, pressure and speed settings. The implementation of differentiated control settings needs to consider the operating limits of the equipment and the physical properties of the materials to ensure the authenticity and practicality of the simulation environment. By systematically testing different combinations of temperature, pressure and speed, it is possible to predict how variables interact with each other and their specific impact on product output, which can be used for subsequent simulation tests to optimize production processes and improve product quality, and generate simulation test configurations.

[0118] S402: Based on the simulation test configuration, run the simulation test to collect product quality data under each control setting, including structural integrity, durability, and the ratio of compliance with performance indicators. The execution process of obtaining the simulation test data is as follows;

[0119] Run simulation tests, which are designed to collect and analyze product quality data under different control settings, including structural integrity, durability, and the ratio of compliance with performance indicators. Data collection is performed through high-precision simulation tools to ensure the accuracy and reliability of each data. By comprehensively evaluating product performance under different control settings, it will provide an empirical basis for further analysis of product behavior and performance under various production settings. It is a key step in optimizing production parameters and improving product quality to obtain simulation test data.

[0120] S403: Using simulation test data, evaluate the impact of various control parameter settings on product quality, and determine the optimal equipment control strategy by comparing the test results with the degree of compliance with production standards. The execution process of generating simulation prediction results is as follows;

[0121] Using simulation test data, evaluate the impact of various control parameter settings on product quality according to the formula:

[0122]

[0123] Calculate the product quality compliance rate, where Q represents the product quality compliance rate, N s is the number of products that meet the production standards, N d The number of products that do not meet the standards;

[0124] In a set of tests, N s =500, N d =50;

[0125] The product quality compliance rate is calculated as

[0126] The results show that under the current control settings, most products can meet the preset quality standards, the optimal equipment control strategy can be determined based on the data, and the generated simulation prediction results will provide a scientific basis for production adjustments to ensure the optimization of product quality and production efficiency.

[0127] See also Figure 6 , using the simulation prediction results, adjusting the settings of the silver wire bonding equipment through selective control, updating the production parameters in real time to optimize the bonding process, and generating the optimized control plan in the following steps:

[0128] S501: According to the simulation prediction results, the optimal temperature, pressure and speed settings are identified, and the parameters of the silver wire bonding equipment in the production environment are adjusted. The execution process of generating a real-time parameter adjustment plan is as follows;

[0129] Based on the simulation prediction results, the optimal temperature, pressure and speed settings are identified according to the formula:

[0130] T opt =T avg +k T ×σ T

[0131] and

[0132] P opt =P avg -k P ×σ P

[0133] and

[0134] S opt =S avg +k S ×σ S

[0135] Update the parameters of the silver wire bonding equipment in the actual production environment, where T opt , P opt , S opt Represent the optimal temperature, pressure and speed settings, T avg , P avg , S avg is the mean value in the test data, σ T , σ P , σ S are the standard deviation of the test data, k T , k P , k S is the adjustment factor;

[0136] Set the average temperature T avg =150℃, average pressure P avg=30kPa, average speed S avg =1500rpm, standard deviation is σ T =5℃,σ P =2kPa,σ S =50rpm, adjustment factor k T =-1, k P =1, k S =0.5;

[0137] The optimal parameters are calculated as:

[0138] T opt =150-5=145℃;

[0139] P opt =30+2=32kPa;

[0140] S opt =1500+25=1525rpm;

[0141] This indicates that the adjusted parameters are more suitable for current production needs, and the generated real-time parameter adjustment plan can optimize equipment performance and production efficiency.

[0142] S502: Based on the real-time parameter adjustment scheme, the equipment is programmed through the control software to automatically adjust the temperature, pressure and operating speed of the equipment, verify that each parameter can achieve the predetermined optimal effect, and generate the execution process of the control parameter implementation record as follows;

[0143] The process includes writing and deploying equipment control code to ensure the precise implementation of each control instruction. Software programming should take into account the physical and operational limitations of the equipment, as well as interface compatibility. It verifies that each parameter can achieve the predetermined optimal effect. This is completed through a real-time feedback mechanism and monitoring system to ensure that the adjustment measures can be evaluated immediately. The parameter value, time and execution results of each adjustment are recorded to provide a basis for subsequent maintenance and fault diagnosis, and to generate control parameter implementation records.

[0144] S503: Apply the adjustment content in the control parameter implementation record to the production line, monitor the adjusted equipment operation status and production quality, update the production parameters in real time to optimize the entire bonding process, and generate the execution process of the optimized control plan as follows;

[0145] Based on real-time data collection, analysis and feedback, and by continuously monitoring the operating data of equipment on the production line, production parameters can be precisely adjusted to ensure the continuity and stability of the production process. The adjusted equipment can operate under optimal operating conditions to improve product consistency and quality, including specific adjustment parameters, implementation time and effect evaluation. The information will be used for further production process improvement and quality control to generate optimized control plans.

[0146] See also Figure 7 , implement the optimization control plan, continuously monitor the key performance indicators of the silver bonding wire equipment, including the operating efficiency of the silver bonding wire equipment and the product qualification rate, and evaluate the performance difference of the silver bonding wire equipment before and after the improvement. The specific steps to obtain the control effect evaluation results are as follows:

[0147] S601: Implement the optimization control plan, continuously monitor the operating efficiency and product qualification rate of the silver wire bonding equipment, and generate the execution process of the performance monitoring data set as follows;

[0148] The process involves the regular collection and analysis of equipment operating data, including speed, temperature, pressure and key performance indicators. The monitoring system continuously evaluates the equipment's operating status to ensure that all parameters are operating within the optimal range, reflecting the equipment's operating efficiency and the product's qualification rate, providing a basis for further data analysis and generating a performance monitoring data set.

[0149] S602: Using the performance monitoring data set, principal component analysis technology is used to compare the operating efficiency and product qualification rate before and after optimization, identify the key factors of performance optimization, and obtain the performance comparison analysis results. The execution process is as follows;

[0150] The formula for the principal component analysis technique is as follows:

[0151]

[0152] Where E is the performance evaluation index, EF represents the optimization rate of operating efficiency, PR represents the optimization rate of product qualification rate, Var(D) represents the variance of monitoring parameter variation in the data set, IR represents the risk ratio in performance optimization, ω1, ω2, ω3 and ω4 are weight coefficients;

[0153] Parameter details and calculation process:

[0154] EF (operation efficiency improvement rate) is calculated by comparing the running time of the equipment before and after optimization. If the running time before optimization is 120 minutes and after optimization is 100 minutes, the EF is calculated as:

[0155]

[0156] PR (product qualification rate improvement rate) is also calculated by the product qualification rate before and after optimization. Assuming that the qualification rate before optimization is 90% and after optimization is 95%, the PR is:

[0157]

[0158] Var(D) (variance of monitoring parameter variation) sets the data variation obtained from multiple measurements, such as the variance of temperature and pressure readings, which is designed to be 0.05;

[0159] IR (unexpected risk ratio) is set to the equipment failure rate monitored in real time. If the number of failures monitored in a month accounts for 0.02 of the total number of operations, the IR is 0.02;

[0160] The weight coefficients ω1, ω2, ω3, and ω4 are set according to the importance of the influence, and ω1 = 0.5, ω2 = 0.3, ω3 = 0.1, and ω4 = 0.1 are set, which reflects the contribution of each factor to the overall performance evaluation;

[0161] Substitute the values ​​into the formula to calculate:

[0162]

[0163] The results show that the comprehensive performance evaluation index has been significantly improved, indicating that the optimization measures have significantly improved the operating efficiency of the equipment and the product qualification rate. The indicators help the analysis team identify and verify the key factors for performance optimization and further formulate targeted improvement measures.

[0164] S603: According to the performance comparison analysis results, the overall control strategy effect is evaluated, the performance optimization of the control scheme is identified and adjusted, and the execution process of generating the control effect evaluation result is as follows;

[0165] The process involves comprehensive consideration of all performance indicators, such as operating efficiency, product qualification rate and maintenance cost, and identifying which control strategies play a decisive role in improving performance. The evaluation is not only based on quantitative data, but also should be combined with actual observations of the production site and feedback from operators. Through comprehensive information, the performance differences before and after the implementation of each control strategy are recorded in detail, and decision-making information on whether further adjustment of the control strategy is needed is provided to ensure that the equipment continues to operate in the best state, ensure the continuous improvement of production efficiency and product quality, and generate control effect evaluation results.

[0166] See also Figure 8 , an electric vehicle state monitoring system is provided, the electric vehicle state monitoring system is used to execute the above electric vehicle state monitoring method, the system comprises:

[0167] The data analysis module collects the temperature readings, pressure values ​​and mechanical running speed of the silver wire bonding equipment, updates the data synchronously, and generates a real-time data set;

[0168] The abnormal data identification module, based on the real-time data set, screens the temperature, pressure and speed data that deviate from the normal production data, records the deviated data points, and marks them as abnormal to obtain the abnormal marking results;

[0169] The parameter optimization module adjusts the control parameters of temperature and pressure according to the abnormal marking results, sets the speed range of the silver wire bonding equipment, matches the production standards, and obtains the adjustment parameter records;

[0170] The scenario simulation module uses adjustment parameter records and differentiated control strategies to simulate multiple scenarios, evaluate product quality under differentiated scenarios, and generate simulation prediction results;

[0171] The strategy execution module uses simulation prediction results to optimize the control settings of the silver wire bonding equipment, update the production control parameters in real time, and monitor key performance indicators, including equipment operating efficiency and product qualification rate, continuously evaluate the improvement effects, and obtain control effect evaluation results.

[0172] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A selective control method for intelligent silver wire bonding equipment based on big data, characterized in that: The following steps are involved: Collect real-time data of silver wire bonding equipment, including temperature readings, pressure values, and mechanical operating speed, and update and synchronize data to generate real-time data sets of equipment; Through the real-time data set of the equipment, big data is used to extract normal production data, analyze deviations in temperature, pressure and speed, automatically identify abnormal data in the production process, and obtain production data analysis results; According to the production data analysis results, selectively adjust the control parameters of the silver bonding wire equipment, adjust the temperature and pressure, set a new range of the speed of the silver bonding wire equipment, match the production standard, and generate a parameter adjustment record; Based on the parameter adjustment records, simulate the impact of differentiated control settings on product quality, evaluate the impact and effect of multiple operation options by testing differentiated control settings, and obtain simulation prediction results; Using the simulation prediction results, adjusting the settings of the silver wire bonding equipment through selective control, updating the production parameters in real time to optimize the bonding process, and generating an optimized control plan; The optimization control scheme is implemented to continuously monitor the key performance indicators of the silver wire bonding equipment, including the operating efficiency of the silver wire bonding equipment and the product qualification rate, and the performance difference of the silver wire bonding equipment before and after the improvement is evaluated to obtain the control effect evaluation result.

2. The method for selective control of intelligent silver wire bonding equipment based on big data according to claim 1 is characterized in that: The real-time data set of the equipment includes the temperature readings, pressure values, and mechanical operating speeds of the bonding silver wire equipment; the production data analysis results include the temperature standard deviation, pressure anomalies, and speed fluctuation data in the production process; the parameter adjustment records include the temperature adjustment value, the pressure adjustment range, and the set speed update parameters; the simulation prediction results include the impact data of differentiated control settings on product quality and the effect comparison of operation selections; the optimization control scheme includes real-time optimization of control parameters, adjustment schemes for equipment settings, and update records of production parameters; the control effect evaluation results include the improvement data of the operating efficiency of the bonding silver wire equipment, optimization records of product qualification rates, and evaluation and analysis of performance improvements.

3. The method for selective control of intelligent silver wire bonding equipment based on big data according to claim 1, characterized in that: Collect real-time data of the silver bonding wire equipment, including temperature readings, pressure values, and mechanical operating speed, and update and synchronize the data. The steps to generate the real-time data set of the equipment are as follows: Collect real-time data of the silver wire bonding equipment, install temperature sensors on the heating parts of the silver wire bonding equipment, fix pressure sensors on the pressing parts of the silver wire bonding equipment, set the sensors to upload data at fixed time intervals, including temperature readings and pressure values, and generate real-time monitoring data sets; Using the real-time monitoring data set, abnormal points in the real-time monitoring data are identified by setting a safe operating temperature range and a pressure threshold, and if the temperature exceeds a preset peak value and the pressure is lower than a trough safety value, an updated data set is obtained; The update data set is used to broadcast the update data to the terminal devices connected to the network, and the current operation status data is received in real time to generate a real-time data set for the device.

4. The method for selective control of intelligent silver wire bonding equipment based on big data according to claim 1 is characterized in that: The steps of extracting normal production data, analyzing temperature, pressure and speed deviations, and automatically identifying abnormal data in the production process by using the real-time data set of the equipment and big data to obtain the production data analysis results are as follows: Through the real-time data set of the equipment, using big data, the production data marked as normal is screened, including normal value interval data of temperature, pressure and speed, to obtain a standard production data set; Based on the standard production data set, compare the deviations of the data points in the equipment real-time data set with the standard production data, set the deviation threshold, identify the abnormal points of temperature, pressure and speed, and generate abnormal data identification results; The abnormal data identification results are used to statistically analyze the frequency and distribution of abnormal data in the entire production process, and the time point when the abnormality occurs and the production rhythm are analyzed in combination with the timestamp, and the production process is optimized to obtain the production data analysis results.

5. The method for selective control of intelligent silver wire bonding equipment based on big data according to claim 1, characterized in that: According to the production data analysis results, selectively adjust the control parameters of the silver wire bonding equipment, adjust the temperature and pressure, set a new range of the speed of the silver wire bonding equipment, match the production standard, and generate the parameter adjustment record in the following steps: Using the production data analysis results, identify the control parameters that need to be adjusted, adjust the abnormal temperature and pressure points, update the current temperature upper limit and pressure lower limit, and generate a temperature and pressure adjustment plan; Based on the temperature and pressure adjustment scheme, the response surface method is used to set the speed range of the silver wire bonding equipment, verify the matching degree between the speed setting and the adjusted temperature and pressure parameters, and generate the optimal speed adjustment scheme; According to the optimal speed adjustment scheme, combined with the temperature and pressure adjustment scheme, the silver wire bonding equipment is selectively controlled, the operation interface and control logic of the silver wire bonding equipment are updated, and the changed parameters and execution time are recorded to generate parameter adjustment records.

6. The method for selective control of intelligent silver wire bonding equipment based on big data according to claim 5 is characterized in that: The response surface methodology formula is as follows: Among them, S is the speed adjustment value, T represents the temperature value, P represents the pressure value, V represents the original equipment speed value, K T , K P and K V Represent the adjustment coefficients for temperature, pressure and speed respectively.

7. The method for selective control of intelligent silver wire bonding equipment based on big data according to claim 1, characterized in that: Based on the parameter adjustment records, the steps of simulating the impact of differentiated control settings on product quality, evaluating the impact and effect of various operation options by testing differentiated control settings, and obtaining simulation prediction results are as follows: Using the parameter adjustment record, setting up a virtual simulation environment to simulate actual production conditions, and applying a variety of differentiated control settings, including differentiated combinations of temperature, pressure, and speed, to generate a simulated test configuration; Based on the simulation test configuration, run a simulation test to collect product quality data under each control setting, including structural integrity, durability, and a ratio that meets performance indicators, and obtain simulation test data; The simulation test data is used to evaluate the impact of various control parameter settings on product quality, and the optimal equipment control strategy is determined by comparing the degree of compliance between the test results and the production standards, generating simulation prediction results.

8. The method for selective control of intelligent silver wire bonding equipment based on big data according to claim 1, characterized in that: Using the simulation prediction results, the settings of the silver wire bonding equipment are adjusted through selective control, and the production parameters are updated in real time to optimize the bonding process. The steps of generating the optimization control plan are as follows: According to the simulation prediction results, the optimal temperature, pressure and speed settings are identified, parameters of the silver wire bonding equipment in the production environment are adjusted, and a real-time parameter adjustment plan is generated; Based on the real-time parameter adjustment scheme, the device is programmed through the control software to automatically adjust the temperature, pressure and operating speed of the device, verify that each parameter can achieve the predetermined optimal effect, and generate a control parameter implementation record; Apply the adjustment content in the control parameter implementation record to the production line, monitor the adjusted equipment operation status and production quality, update the production parameters in real time to optimize the entire bonding process, and generate an optimized control plan.

9. The method for selective control of intelligent silver wire bonding equipment based on big data according to claim 1, characterized in that: The steps of implementing the optimization control scheme, continuously monitoring the key performance indicators of the silver wire bonding equipment, including the operation efficiency of the silver wire bonding equipment and the product qualification rate, and evaluating the performance difference of the silver wire bonding equipment before and after the improvement, and obtaining the control effect evaluation results are as follows: Implement the optimization control scheme, continuously monitor the operating efficiency and product qualification rate of the silver wire bonding equipment, and generate a performance monitoring data set; By using the performance monitoring data set, principal component analysis technology is used to compare the operating efficiency and product qualification rate before and after optimization, identify the key factors of performance optimization, and obtain performance comparison analysis results; The formula for the principal component analysis technique is as follows: Where E is the performance evaluation index, EF represents the optimization rate of operating efficiency, PR represents the optimization rate of product qualification rate, Var(D) represents the variance of monitoring parameter variation in the data set, IR represents the risk ratio in performance optimization, ω1, ω2, ω3 and ω4 are weight coefficients; Based on the performance comparison and analysis results, the overall control strategy effect is evaluated, the performance optimization of the adjusted control scheme is identified, and the control effect evaluation result is generated.

10. Intelligent silver wire bonding equipment selective control system based on big data, characterized in that: According to the method for selective control of intelligent silver wire bonding equipment based on big data according to any one of claims 1 to 9, the system comprises: The data analysis module collects the temperature readings, pressure values ​​and mechanical running speed of the silver wire bonding equipment, updates the data synchronously, and generates a real-time data set; The abnormal data identification module screens the temperature, pressure and speed data that are deviated from the conventional production data based on the real-time data set, records the deviated data points, and marks them as abnormal to obtain an abnormal marking result; The parameter optimization module adjusts the control parameters of temperature and pressure according to the abnormal marking result, sets the speed range of the silver wire bonding equipment, matches the production standard, and obtains the adjustment parameter record; The scenario simulation module uses the adjustment parameter records and differentiated control strategies to perform multi-scenario simulations, evaluate product quality under differentiated scenarios, and generate simulation prediction results; The strategy execution module uses the simulation prediction results to optimize the control settings of the silver wire bonding equipment, update the production control parameters in real time, and monitor key performance indicators, including equipment operating efficiency and product qualification rate, continuously evaluate the improvement effect, and obtain the control effect evaluation results.

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