A real-time analysis method and system for PCB production data

Abstract

The present invention relates to the field of PCB production technology, and specifically to a real-time analysis method and system for PCB production data, comprising the following steps: based on the production and packaging process of the PCB boards, collecting PCB board size data and weight data, using a random forest algorithm to evaluate the stacking efficiency of the PCB boards, and optimizing stacking parameters to obtain a stacking efficiency optimization index. The present invention, by analyzing the size and weight data of the PCB boards in real time and using a random forest algorithm, can accurately evaluate and optimize the stacking efficiency, thereby improving resource utilization and reducing production waste. Time series analysis is applied to humidity card and desiccant usage data to achieve more precise control of the production environment, ensuring product quality consistency and reliability during the production process. Using a CNN network to analyze the operation data of a robot arm not only improves operational efficiency but also enhances the production line's adaptability to emergencies. Real-time regression analysis of thermal parameters optimizes the packaging process and ensures packaging quality.

Description

Technical Field

[0001] The present invention relates to the technical field of PCB production, and in particular to a method and system for real-time analysis of PCB production data. Background Art

[0002] PCB production technology involves all steps from designing, manufacturing to testing printed circuit boards (PCBs). Printed circuit boards are an indispensable part of electronic devices. PCBs provide physical support and electrical connections between electronic components. This includes the design of multi-layer PCBs, material selection, drawing of circuit diagrams, and the application of chemical and physical processes to manufacture PCBs. With technological advancements, PCB production is increasingly focused on improving production efficiency and reducing costs while ensuring product quality and reliability.

[0003] Among them, the real-time analysis method of PCB production data focuses on real-time collection and analysis of data in the PCB production process to optimize the production process and improve production efficiency. By using sensors, data acquisition and data analysis technology, it can instantly monitor various parameters on the production line, such as temperature, pressure, and current, etc., so as to detect anomalies in the production process in real time and make adjustments quickly. This not only helps to reduce the scrap rate in the production process, but also ensures the consistency and quality of the products, and improves the automation and intelligence level of the production line.

[0004] Existing technologies lack the ability to respond to real-time data feedback, resulting in untimely or inaccurate adjustments when production conditions or environmental parameters change. For example, although the operating parameters of the robot can be automatically adjusted, without the support of deep learning, its adjustments will not be optimized for complex or non-standard situations. Existing technologies are also insufficient in the timeliness of monitoring small changes in temperature and pressure, resulting in products being packaged before the optimal packaging conditions are met, affecting product quality and hindering the maximum optimization of the production process. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a real-time analysis method and system for PCB production data.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a real-time analysis method for PCB production data, comprising the following steps:

[0007] S1: Based on the production and packaging process of PCB boards, PCB board size and weight data are collected, and the random forest algorithm is used to evaluate the PCB board stacking efficiency and optimize the stacking parameters to obtain the stacking efficiency optimization index;

[0008] S2: Based on the stacking efficiency optimization index, a time series analysis is performed on the usage data of the humidity card and desiccant to evaluate the impact on the PCB packaging environment. Based on the impact analysis results, the humidity card and desiccant placement mechanism is adjusted to obtain environmental control parameters.

[0009] S3: Based on the environmental control parameters, the operating data of the six-axis robot and the three-axis robot are analyzed, and the CNN network is used to identify operational efficiency issues in the PCB board packaging process. The robot control parameters are continuously monitored and adjusted to generate an efficiency optimization configuration.

[0010] S4: Based on the efficiency optimization configuration, regression analysis is performed on the heat shrink packaging temperature data and the heat shrink packaging pressure data to determine the optimal thermal parameters. The optimal thermal parameters are applied to the packaging line, and the PCB board packaging process is monitored and adjusted in real time to obtain a production data analysis log.

[0011] The present invention is improved in that the steps for obtaining the stacking efficiency optimization index are specifically as follows:

[0012] S111: Based on the production and packaging process of PCB boards, collect the size and weight data of PCB boards, merge the data, and build an integrated data set;

[0013] S112: Based on the integrated data set, the stacking efficiency of the PCB board is evaluated by a random forest algorithm using the formula:

[0014]

[0015] Get the whole stack efficiency evaluation result, where ED represents the whole stack efficiency of the PCB board, w i represents the weight of the i-th data point, RF(X i ) is the random forest for the i-th data point X i The efficiency output of , n is the total number of data points;

[0016] S113: According to the stacking efficiency evaluation result, stacking parameters are adjusted to optimize the stacking efficiency and obtain a stacking efficiency optimization index.

[0017] The present invention is improved in that the step of evaluating the impact on the PCB board packaging environment is specifically as follows:

[0018] S211: Based on the stacking efficiency optimization index, collect usage data of humidity cards and desiccants during the PCB packaging process, including usage type, quantity, and duration, to obtain usage data records;

[0019] S212: Perform time series analysis on the usage data records to calculate the impact of the humidity card and desiccant on the packaging environment humidity using the formula:

[0020]

[0021] The time series analysis results are obtained, where ΔHS represents the change in ambient humidity, hs i is the influence coefficient of the ith humidity card or desiccant, qs i is the usage of the i-th humidity card or desiccant, α s is the environmental sensitivity adjustment factor, n s is the number of material types;

[0022] S213: Based on the time series analysis results, a comparative analysis is performed with the PCB damage data to evaluate the effects of the humidity card and the desiccant, thereby obtaining a packaging environmental impact assessment result.

[0023] The present invention is improved in that the step of obtaining the environmental control parameters is specifically as follows:

[0024] S221: Adjusting the humidity card and desiccant delivery mechanism based on the impact analysis results, including adjusting the delivery amount and frequency of the humidity card and desiccant, optimizing the humidity control of the PCB packaging environment, and obtaining new humidity control parameters;

[0025] S222: Based on the new humidity control parameters, the PCB board packaging process is tested to monitor the change of environmental humidity and the damage rate of the PCB boards to obtain environmental control parameters.

[0026] The present invention is improved in that the steps of analyzing the operating data are specifically as follows:

[0027] S311: Based on the environmental control parameters, collect operation data of the six-axis and three-axis manipulators, including operation speed, accuracy, and fault frequency, to obtain a manipulator performance data set;

[0028] S312: Based on the manipulator performance data set, the CNN network is used to process the manipulator speed and accuracy data to evaluate the impact of environmental parameter changes on the manipulator performance using the formula:

[0029] RB i =a Q +b Q ·SF i +c Q ·PF i

[0030] Identify key influencing factors and operational efficiency issues and obtain robot analysis results, where RB i represents the robot response time of the i-th data point, SF i is the manipulator operation speed recorded at the i-th data point, PF iis the manipulator operation accuracy recorded at the i-th data point, a Q is a constant term, b Q is the velocity coefficient, c Q is the accuracy coefficient.

[0031] The present invention is improved in that the steps of obtaining the efficiency optimization configuration are specifically as follows:

[0032] S321: Regularly collect operation data of the six-axis and three-axis manipulators, including operation speed, accuracy, and fault frequency, monitor the performance of the current manipulator control parameters, and obtain a monitoring data set;

[0033] S322: Analyze the monitoring data set, adjust the robot control parameters, including speed setting or accuracy adjustment, and re-evaluate the performance of the robot, compare the operation data before and after the adjustment, determine whether the target efficiency is achieved, and obtain the efficiency optimization configuration.

[0034] The present invention is improved in that the step of determining the optimal thermal parameters is specifically as follows:

[0035] S411: Based on the efficiency optimization configuration, collect temperature and pressure data during the heat shrink packaging process, including values ​​under various packaging conditions, to obtain a temperature and pressure data set;

[0036] S412: Apply regression analysis to process the temperature and pressure data set using the formula:

[0037] VP opt =a O +b O VT+c O VPr

[0038] Analyze the impact of parameters on packaging quality and determine the optimal thermal parameters, among which VP opt represents the optimal packaging performance, VT is the temperature setting during packaging, VPr is the pressure applied during packaging, and a O is a constant term, b O is the regression coefficient of temperature, c O is the regression coefficient of pressure.

[0039] The present invention is improved in that the steps of obtaining the production data analysis log are specifically as follows:

[0040] S421: Based on the optimal thermal parameters, the PCB board packaging process is monitored in real time, and real-time data of temperature and pressure and associated packaging data are collected to obtain real-time monitoring data records;

[0041] S422: Analyze the collected real-time monitoring data records to evaluate data trends and anomalies using the formula:

[0042] LH eff =δ M ·(WH t -WH t-1 )+γ M ·PH eff

[0043] Analyze the impact on production efficiency and obtain production data analysis log, where LH eff Indicates log efficiency, WH t Indicates the operating temperature parameter at the current time point, WH t-1 Indicates the operating temperature parameter at the previous time point, PH eff represents process efficiency, δ M and γ M is the adjustment factor.

[0044] A real-time analysis system for PCB production data, comprising:

[0045] The stacking efficiency module collects PCB board size and weight data based on the PCB production and packaging process, uses the random forest algorithm to analyze the PCB board stacking efficiency, adjusts the stacking parameters, and obtains the stacking efficiency optimization index;

[0046] The environmental control module performs a time series analysis on the usage data of the humidity card and the desiccant based on the stacking efficiency optimization index to evaluate the impact on the PCB packaging environment. Based on the impact analysis results, the module adjusts the placement mechanism of the humidity card and the desiccant to obtain environmental control parameters.

[0047] The robot optimization module analyzes the operating data of the six-axis robot and the three-axis robot based on the environmental control parameters, uses the CNN network to identify operational efficiency issues in the PCB packaging process, and monitors and adjusts the robot control parameters to generate an efficiency-optimized configuration.

[0048] The thermal parameter setting module performs regression analysis on the heat shrink packaging temperature data and the heat shrink packaging pressure data based on the efficiency optimization configuration to determine the optimal thermal parameters;

[0049] The monitoring and recording module applies the optimal thermal parameters to the packaging line, monitors and adjusts the packaging process of the PCB board in real time, and obtains a production data analysis log.

[0050] Compared with the prior art, the advantages and positive effects of the present invention are:

[0051] In the present invention, by real-time analysis of PCB board size and weight data, a random forest algorithm is used to accurately evaluate and optimize stacking efficiency, thereby improving resource utilization and reducing production waste. Time series analysis is applied to humidity card and desiccant usage data to achieve more precise regulation of the production environment, ensuring product quality consistency and reliability during the production process. Analyzing the robot's operating data through a CNN network not only improves operational efficiency but also enhances the production line's adaptability to emergencies. Real-time regression analysis of thermal parameters optimizes the packaging process, ensures packaging quality, and reduces rework and waste generation. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 The present invention proposes a flow chart of a method for real-time analysis of PCB production data;

[0053] Figure 2 This is a flow chart for obtaining the optimization index of the stacking efficiency in the present invention;

[0054] Figure 3 This is a flow chart for evaluating the impact on the PCB board packaging environment in the present invention;

[0055] Figure 4 This is a flow chart for obtaining environmental control parameters in the present invention;

[0056] Figure 5 This is a flow chart for analyzing operating data in the present invention;

[0057] Figure 6 A flowchart for obtaining the efficiency optimization configuration in the present invention;

[0058] Figure 7 Flow chart for determining the optimal thermal parameters in the present invention;

[0059] Figure 8 This is a flowchart for obtaining production data analysis logs in the present invention. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0061] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings and are only for the convenience of describing the present invention and simplifying the description. They do not indicate or imply that the devices or elements referred to must have a specific direction, be constructed and operate in a specific direction, and therefore should not be understood as limiting the present invention. In addition, in the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0062] Example

[0063] See also Figure 1 The present invention provides a technical solution: a real-time analysis method for PCB production data, comprising the following steps:

[0064] S1: Based on the production and packaging process of PCB boards, PCB board size and weight data are collected, and the random forest algorithm is used to evaluate the PCB board stacking efficiency and optimize the stacking parameters to obtain the stacking efficiency optimization index;

[0065] S2: Based on the stacking efficiency optimization index, a time series analysis of humidity card and desiccant usage data is conducted to evaluate the impact on the PCB packaging environment. Based on the impact analysis results, the humidity card and desiccant placement mechanism is adjusted to obtain environmental control parameters.

[0066] S3: Based on environmental control parameters, the operating data of the six-axis robot and the three-axis robot are analyzed. Using the CNN network, operational efficiency issues in the PCB packaging process are identified. The robot control parameters are continuously monitored and adjusted to generate an efficiency-optimized configuration.

[0067] S4: Based on the efficiency optimization configuration, regression analysis is performed on the heat shrink packaging temperature data and heat shrink packaging pressure data to determine the optimal thermal parameters. The optimal thermal parameters are applied to the packaging line, and the PCB board packaging process is monitored and adjusted in real time to obtain a production data analysis log.

[0068] The overall stack efficiency optimization indicators include stacking height standards, area utilization, and weight distribution balance. The environmental control parameters include humidity maintenance range, desiccant absorption capacity, and humidity recovery time. The efficiency optimization configuration includes response time optimization results, automatic fault diagnosis results, and energy efficiency ratio. The production data analysis log includes temperature monitoring indicators, pressure change data, and adjustment feedback frequency.

[0069] See also Figure 2 The specific steps for obtaining the stack efficiency optimization index are as follows:

[0070] S111: Based on the production and packaging process of PCB boards, collect the size and weight data of PCB boards, merge the data, and build an integrated data set;

[0071] Dimensional data is detected by automated measuring equipment, and weight data is obtained through an online weighing system. The measuring equipment includes a laser ranging sensor and a high-precision electronic scale. The data collected by the sensor is stored in the form of raw signals, and the raw signals are cleaned to remove duplicate values ​​and noise data. The cleaned data is stored in an integrated data pool in a standard format. At the same time, the stored data is classified and indexed according to board number, batch and timestamp for subsequent processing. Finally, the integration of PCB board size and weight data is completed to form an integrated data set for analysis.

[0072] S112: Based on the integrated data set, the random forest algorithm is used to evaluate the stacking efficiency of PCB boards using the formula:

[0073]

[0074] Get the whole stack efficiency evaluation result, where ED represents the whole stack efficiency of the PCB board, w i Indicates the weight of the i-th data point, reflecting the importance of the data point in the evaluation of the stacking efficiency. i ) is the random forest for the i-th data point X i Efficiency output, X i Include all relevant features of the data point, such as size and weight, where n is the total number of data points;

[0075] There are three PCB board data points with weights w1=0.5, w2=1.5, and w3=0.8. The efficiency outputs of random forest for the data points are RF(X1)=0.75, RF(X2)=0.85, and RF(X3)=0.65. Substitute them into the formula:

[0076]

[0077] The results show that by considering the specific weights and efficiencies of different PCB boards, a weighted stack efficiency of 1.23 can be obtained, indicating that the overall efficiency is relatively high, which improves the accuracy and adaptability of the evaluation.

[0078] S113: Adjusting stacking parameters according to the stacking efficiency evaluation result to optimize the stacking efficiency and obtain a stacking efficiency optimization index;

[0079] Based on the stacking efficiency evaluation data, key influencing factors are extracted, such as stacking height, plate arrangement, vertical pressure and other parameters. A sensitivity analysis is conducted on the influencing factors to identify the parameters with the greatest impact on efficiency fluctuations. Combined with on-site test data, the stacking height and arrangement method are optimized, and the impact of different parameter combinations on the stacking efficiency is tested. Regression analysis is used to quantify the efficiency improvement effect of the adjustment and determine the optimal parameter combination. The optimized parameter values ​​are input into the control system of the production line, the stacking settings of the production equipment are adjusted, and the efficiency changes after adjustment are monitored in real time to form an optimized stacking efficiency indicator.

[0080] See also Figure 3 , the specific steps to evaluate the impact on the PCB packaging environment are:

[0081] S211: Based on the whole stacking efficiency optimization index, collect the usage data of humidity cards and desiccant during the PCB packaging process, including the usage type, quantity and duration, and obtain usage data records;

[0082] The usage type, quantity and duration of humidity cards and desiccants are obtained in real time. A unique identifier needs to be set during the data collection process to distinguish the usage records of different batches. All data are summarized after collection. By calling the data interface, the type of humidity card, moisture absorption capacity and desiccant activity are classified and labeled, and the correlation analysis of various data is supplemented in combination with time nodes. Further usage data records including the frequency, duration and quantity statistics of various humidity control materials are generated. This record provides direct data support for subsequent analysis.

[0083] S212: Perform time series analysis on the usage data records and calculate the impact of humidity card and desiccant on the packaging environment humidity using the formula:

[0084]

[0085] The time series analysis results are obtained, where ΔHS represents the change in ambient humidity, hs i is the influence coefficient of the ith humidity card or desiccant, qs i is the usage of the i-th humidity card or desiccant, α s is the environmental sensitivity adjustment factor, which is used to adjust the calculation results to better reflect the effect of humidity control measures under specific environmental conditions. s is the number of material types;

[0086] There are three different humidity control materials. The usage and influence coefficient of each material are as follows: hs1 = 0.3, qs1 = 50, hs2 = 0.45, qs2 = 30, hs3 = 0.25, qs3 = 20, and the environmental sensitivity adjustment coefficient α s =1.2, the calculation process is:

[0087] ΔHS=1.2×[(0.3×50)+(0.45×30)+(0.25×20)]

[0088] ΔHS=1.2×[15+13.5+5]

[0089] ΔHS=1.2×33.5

[0090] ΔHS=40.2

[0091] The results show that the change in ambient humidity is 40.2, considering the use of various humidity control materials. This indicates that under the current humidity control strategy, humidity control measures have a significant impact on the environment, providing a direct basis for adjusting the strategy.

[0092] S213: Based on the time series analysis results, a comparative analysis is performed with the PCB damage data to evaluate the effects of the humidity card and desiccant, and obtain the packaging environmental impact assessment results;

[0093] By analyzing the relationship between humidity changes in the packaging environment and PCB damage data, the humidity change data was matched item by item with the PCB damage rate. By statistically analyzing the damage rate corresponding to humidity changes in historical data, the impact of humidity changes on PCB protection performance was analyzed. By constructing a tabular comparative analysis template, the damage probability under different humidity ranges was grouped and evaluated. Combined with the specific failure type data in the damage records, the effect of humidity control materials on improving the humidity in the packaging environment was extracted. A summary analysis table of the effectiveness of using humidity cards and desiccant was generated, forming a complete packaging environmental impact assessment result.

[0094] See also Figure 4 , the specific steps for obtaining environmental control parameters are:

[0095] S221: Based on the impact analysis results, adjust the humidity card and desiccant delivery mechanism, including adjusting the amount and frequency of humidity card and desiccant delivery, optimize the humidity control of the PCB packaging environment, and obtain new humidity control parameters;

[0096] Adjusting the humidity card and desiccant delivery mechanism requires comprehensive consideration of multiple factors, including the humidity baseline value of the operating environment, the moisture absorption rate of different types of humidity control materials, and the sensitivity of the PCB board to humidity fluctuations. By analyzing the performance parameters of the humidity card and desiccant, the delivery amount is adjusted. The specific adjustment process includes calculating the daily fluctuation range of the humidity baseline value, selecting the appropriate humidity card type based on the fluctuation range, and estimating the required daily delivery amount based on the moisture absorption rate. Then, the desiccant delivery frequency is optimized based on the actual operation time of the packaging line to generate the humidity control parameter configuration.

[0097] S222: Based on the new humidity control parameters, the PCB packaging process is tested to monitor the change in ambient humidity and the damage rate of the PCB boards, and obtain the environmental control parameters;

[0098] To test environmental changes during PCB packaging, we first monitored actual humidity changes after using humidity cards and desiccant. We compared these changes with the target values ​​in the humidity control parameters to evaluate the effectiveness of the humidity cards and desiccant. We then collected integrity data on the PCBs after packaging. By analyzing this data, we recorded the damage rate of the boards affected by humidity control and compared the damage rate changes before and after adjustment to determine the effectiveness of the new humidity control parameters. Finally, we optimized the environmental control parameters based on the experimental data to ensure their adaptability and operability in practical applications.

[0099] See also Figure 5 , the specific steps for analyzing the running data are:

[0100] S311: Based on the environmental control parameters, collect the operation data of the six-axis and three-axis manipulators, including operation speed, accuracy, and fault frequency, to obtain a manipulator performance data set;

[0101] First, the operating data of the six-axis and three-axis robots are collected in real time through sensors. The data content covers core indicators such as operating speed, accuracy and fault frequency. During the collection process, each operation record of the robot is divided into independent data points. Each data point records the operation time of the robot, the deviation value of the motion trajectory and the type of fault generated during the operation. To ensure data integrity, a redundant collection strategy is used to cross-validate key operating data. At the same time, data with sensor abnormalities or large errors are eliminated. The elimination standard is based on the setting of operating time deviation exceeding the normal range. Through this process, the robot performance data set is obtained, which contains operating performance records under different environmental control parameters, providing multi-dimensional data support required for subsequent analysis.

[0102] S312: Based on the robot performance data set, the CNN network is used to process the robot's speed and accuracy data to evaluate the impact of environmental parameter changes on the robot's performance. The formula is:

[0103] RB i =a Q +b Q ·SF i +c Q ·PF i

[0104] Identify key influencing factors and operational efficiency issues and obtain robot analysis results, where RB i represents the robot response time of the i-th data point, SFi is the manipulator operation speed recorded at the i-th data point, PF i is the manipulator operation accuracy recorded at the i-th data point, a Q is a constant term, which can be regarded as the basic response time, b Q Is the speed coefficient, indicating the speed SF i Response time RB i The impact of c Q Is the accuracy coefficient, indicating the accuracy PF i Response time RB i the impact of;

[0105] The robot's movement speed in a specific environment is 120 mm / s, the accuracy is 98%, the basic response time is 0.5 seconds, the speed influence coefficient is 0.05 seconds, and the accuracy influence coefficient is -0.01 seconds. Substitute the formula for calculation:

[0106] RB i =0.5+0.05·120+(-0.01)·98

[0107] RB i =0.5+6-0.98=5.52

[0108] The results show that at this speed and accuracy, the robot's response time is 5.52 seconds, demonstrating how adjusting speed and accuracy to optimize response time can directly help improve production efficiency and reduce errors.

[0109] See also Figure 6 , the specific steps for obtaining the efficiency optimization configuration are:

[0110] S321: Regularly collect operation data of the six-axis and three-axis manipulators, including operation speed, accuracy, and fault frequency, monitor the performance of the current manipulator control parameters, and obtain a monitoring data set;

[0111] The data of each operation is recorded in real time by sensors installed on the robot, and stored in categories by time, robot type, and task batch. Invalid data and noise are then cleaned up, and speed, accuracy, and failure rate data are uniformly formatted and stored for subsequent analysis. During the monitoring period, performance changes under different environmental conditions are marked and annotated to form data with environmental relevance, resulting in a monitoring data set covering different operating conditions and performance.

[0112] S322: Analyze the monitoring data set, adjust the robot control parameters, including speed setting or accuracy adjustment, and re-evaluate the robot performance. Compare the operation data before and after the adjustment to determine whether the target efficiency is achieved and obtain the efficiency optimization configuration.

[0113] Analyze the speed and accuracy data and perform comparative processing to determine the changing trend of the robot's operating efficiency. First, group and calculate each control parameter, including calculating its mean and variance according to the speed range and accuracy range. Then compare the failure frequency of each group, analyze the impact of the speed and accuracy ratio on the operating efficiency, and then perform a difference analysis on the robot operation data before and after adjustment to determine whether the adjustment is effective. When the efficiency difference exceeds the set threshold, further adjust the control parameters and retest to obtain the efficiency optimization configuration after performance improvement.

[0114] See also Figure 7 , the specific steps for determining the optimal thermal parameters are:

[0115] S411: Based on the efficiency optimization configuration, collect temperature and pressure data during the heat shrink packaging process, including values ​​under various packaging conditions, to obtain a temperature and pressure data set;

[0116] Multiple sets of test conditions were set for heat shrink packaging equipment. The temperature and pressure data of the packaging line were recorded under each set of conditions. Temperature and pressure readings were obtained through real-time monitoring sensors installed on the packaging equipment. Data filtering methods were then used to eliminate noise and invalid data, and the data were classified and stored. The classification criteria included packaging material type, production batch, and equipment type. Each set of data points recorded the specific temperature and pressure values ​​and their corresponding timestamps. The accuracy and representativeness of the data were ensured through repeated experiments. The temperature and pressure data of each set of experiments were integrated to obtain the temperature and pressure data sets.

[0117] S412: Apply regression analysis to the temperature and pressure data sets using the formula:

[0118] VP opt =a O +b O VT+c O VPr

[0119] Analyze the impact of parameters on packaging quality and determine the optimal thermal parameters, among which VP opt Indicates optimal packaging performance, reflecting the optimal packaging quality that can be achieved under a specific temperature and pressure combination. VT is the temperature setting during the packaging process, which directly affects the heat shrinkage effect and packaging quality. VPr is the pressure applied during the packaging process, which is key to the shrinkage rate and sealing strength of the material. O is a constant term representing the baseline packaging effect, i.e., the default packaging performance when there is no temperature and pressure change, b O Is the regression coefficient of temperature, indicating the effect of temperature change on packaging performance VP opt The degree of influence, c O is the regression coefficient of pressure, which indicates the effect of pressure change on packaging performance VP optthe extent of the impact;

[0120] Under a specific set of packaging parameters, the measured temperature VT is 150℃, the pressure VPr is 5 bar, and the constant term a O The temperature coefficient b was determined to be 0.5. O is 0.03, the pressure coefficient c O is 0.02, and the optimal packaging performance is calculated according to the formula:

[0121] VP opt =0.5+0.03·150+0.02·5=0.5+4.5+0.1=5.1

[0122] The results show that at this specific temperature and pressure, a packaging performance index of 5.1 can be achieved. The index reflects the overall quality and efficiency of the packaging, indicating that the effectiveness of the packaging process can be significantly improved by optimizing the temperature and pressure settings.

[0123] See also Figure 8 , the specific steps for obtaining production data analysis logs are:

[0124] S421: Based on the optimal thermal parameters, the PCB board packaging process is monitored in real time, and real-time data of temperature and pressure and associated packaging data are collected to obtain real-time monitoring data records;

[0125] Collect real-time data on temperature and pressure as well as associated packaging effect data. Use temperature sensors and pressure sensors to record temperature and pressure changes every second, and record the sealing strength and material integrity after packaging. After each packaging cycle, classify the data by timestamp, packaging batch, and product number, and perform preliminary data cleaning operations, including removing invalid readings at sensor failure points and calculating outliers in the data range. The processed data is then integrated into a real-time monitoring data record covering the entire production cycle, which includes time series data of all key parameters and their correlation with packaging effects.

[0126] S422: Analyze the collected real-time monitoring data records and evaluate data trends and anomalies using the formula:

[0127] LH eff =δ M ·(WH t -WH t-1 )+γ M ·PH eff

[0128] Analyze the impact on production efficiency and obtain production data analysis log, where LH effRepresents log efficiency, which is used to measure the changes and optimization of packaging process efficiency in production data analysis logs. t Indicates the working temperature parameters at the current time point, including temperature and pressure, etc., reflecting the actual operating conditions of the current packaging process. t-1 Indicates the operating temperature parameters at the previous time point, including temperature and pressure, etc., which are used to compare with the current data and analyze the trend of parameter changes. eff Represents process efficiency, used to quantify the efficiency level in the production process and evaluate the performance of the overall production line, δ M and γ M is the adjustment coefficient, which is used to adjust the contribution of different factors to efficiency;

[0129] If the operating temperature parameter measured at two consecutive time points is WH t =75℃ and WH t-1 =70℃, process efficiency PH eff =0.95, adjustment coefficient δ M = 0.1 and γ M =0.85, then substitute into the formula to calculate:

[0130] LH eff =0.1·(75-70)+0.85·0.95

[0131] LH eff =0.1·5+0.8075=0.5+0.8075=1.3075

[0132] The results show that during the observation period, the logarithm of production efficiency increased by 1.3075 by fine-tuning the temperature settings, indicating that subtle adjustments to the production environment can significantly improve the efficiency of the production process.

[0133] A real-time analysis system for PCB production data, the system comprising:

[0134] The stacking efficiency module collects PCB board size and weight data based on the PCB production and packaging process, uses the random forest algorithm to analyze the PCB board stacking efficiency, adjusts the stacking parameters, and obtains the stacking efficiency optimization index;

[0135] The environmental control module conducts time series analysis on humidity card and desiccant usage data based on the overall stacking efficiency optimization index to assess the impact on the PCB packaging environment. Based on the impact analysis results, it adjusts the humidity card and desiccant placement mechanism to obtain environmental control parameters.

[0136] The robot optimization module analyzes the operating data of the six-axis robot and the three-axis robot based on environmental control parameters. It uses the CNN network to identify operational efficiency issues in the PCB packaging process, monitor and adjust the robot control parameters, and generate an efficiency-optimized configuration.

[0137] The thermal parameter setting module performs regression analysis on the heat shrink packaging temperature data and heat shrink packaging pressure data based on efficiency optimization configuration to determine the optimal thermal parameters;

[0138] The monitoring and recording module applies the optimal thermal parameters to the packaging line, monitors and adjusts the PCB packaging process in real time, and obtains a production data analysis log.

[0139] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A real-time analysis method for PCB production data, characterized in that: The following steps are involved: Based on the production and packaging process of PCB boards, PCB board size and weight data are collected, and a random forest algorithm is used to evaluate the PCB board stacking efficiency. The stacking parameters are optimized to obtain the stacking efficiency optimization index; The steps for obtaining the stacking efficiency optimization index are specifically as follows: Based on the production and packaging process of PCB boards, the size and weight data of PCB boards are collected, the data is merged, and an integrated data set is constructed; Based on the integrated data set, the random forest algorithm is used to evaluate the stacking efficiency of PCB boards using the formula: The whole stack efficiency evaluation results are obtained, among which, Represents the overall stacking efficiency of the PCB board. Indicates the The weight of the data point, Is the random forest for the data points Efficiency output, is the total number of data points; According to the stacking efficiency evaluation result, adjusting the stacking parameters, optimizing the stacking efficiency, and obtaining the stacking efficiency optimization index; Based on the stacking efficiency optimization index, a time series analysis of humidity card and desiccant usage data is performed to evaluate the impact on the PCB packaging environment. Based on the impact analysis results, the humidity card and desiccant placement mechanism is adjusted to obtain environmental control parameters. The steps for evaluating the impact on the PCB packaging environment are as follows: Based on the stacking efficiency optimization index, the usage data of humidity cards and desiccants during the PCB packaging process are collected, including the type, quantity, and duration of use, to obtain usage data records; Perform a time series analysis on the usage data records to calculate the effect of the humidity card and desiccant on the packaging environment humidity using the formula: The time series analysis results are obtained, where Indicates the change in ambient humidity. It is The influence coefficient of humidity card or desiccant, It is The amount of humidity card or desiccant used, is the environmental sensitivity adjustment factor, is the number of material types; Based on the time series analysis results, a comparative analysis is conducted with the PCB damage data to evaluate the effects of the humidity card and desiccant, and obtain the packaging environmental impact assessment results; Based on the environmental control parameters, the operating data of the six-axis robot and the three-axis robot are analyzed. The CNN network is used to identify operational efficiency issues in the PCB board packaging process, and the robot control parameters are continuously monitored and adjusted to generate an efficiency-optimized configuration. The steps of analyzing the operating data are specifically as follows: Based on the environmental control parameters, the operation data of the six-axis and three-axis manipulators are collected, including the operation speed, accuracy, and frequency of failures, to obtain a manipulator performance data set; Based on the robot performance data set, the CNN network is used to process the speed and accuracy data of the robot to evaluate the impact of environmental parameter changes on the robot performance. The formula is: Identify key influencing factors and operational efficiency issues and obtain robot analysis results, including: Representative The robot response time for each data point, It is The robot operation speed recorded by the data points, It is The accuracy of the robot operation recorded by the data points, is a constant term, is the velocity coefficient, is the accuracy coefficient; Based on the efficiency optimization configuration, regression analysis is performed on the heat shrink packaging temperature data and the heat shrink packaging pressure data to determine the optimal thermal parameters. The optimal thermal parameters are applied to the packaging line, and the PCB board packaging process is monitored and adjusted in real time to obtain a production data analysis log; The steps for determining the optimal thermal parameters are specifically as follows: Based on the efficiency optimization configuration, collecting temperature and pressure data during the heat shrink packaging process, including values ​​under various packaging conditions, to obtain a temperature and pressure data set; Regression analysis was applied to the temperature and pressure data sets using the formula: Analyze the impact of parameters on packaging quality and determine the optimal thermal parameters, among which, Indicates the optimal packaging performance, is the temperature setting during the packaging process, is the pressure applied during packaging, is a constant term, is the regression coefficient of temperature, is the regression coefficient of pressure; The steps for obtaining the production data analysis log are specifically as follows: Based on the optimal thermal parameters, the PCB board packaging process is monitored in real time, and real-time data of temperature and pressure as well as associated packaging data are collected to obtain real-time monitoring data records; The collected real-time monitoring data records are analyzed to evaluate data trends and anomalies using the formula: Analyze the impact on production efficiency and obtain production data analysis logs, where: Indicates log efficiency, Indicates the operating temperature parameter at the current time point, Indicates the operating temperature parameter at the previous time point, represents process efficiency, and is the adjustment factor.

2. The method for real-time analysis of PCB production data according to claim 1, characterized in that: The steps for obtaining the environmental control parameters are specifically as follows: Based on the impact analysis results, the humidity card and desiccant delivery mechanism is adjusted, including the amount and frequency of the humidity card and desiccant delivery, to optimize the humidity control of the PCB packaging environment and obtain new humidity control parameters; Based on the new humidity control parameters, the PCB board packaging process is tested to monitor the changes in ambient humidity and the damage rate of the PCB boards to obtain the environmental control parameters.

3. The method for real-time analysis of PCB production data according to claim 1, characterized in that: The steps for obtaining the efficiency optimization configuration are specifically as follows: Regularly collect the operation data of six-axis and three-axis manipulators, including operation speed, accuracy and fault frequency, monitor the performance of the current manipulator control parameters, and obtain monitoring data sets; Analyze the monitoring data set, adjust the robot control parameters, including speed setting or accuracy adjustment, and re-evaluate the performance of the robot. Compare the operating data before and after the adjustment to determine whether the target efficiency is achieved and obtain the efficiency optimization configuration.

4. A real-time analysis system for PCB production data, characterized in that: The method for real-time analysis of PCB production data according to any one of claims 1 to 3 is implemented, wherein the system comprises: The stacking efficiency module collects PCB board size and weight data based on the PCB production and packaging process, uses the random forest algorithm to analyze the PCB board stacking efficiency, adjusts the stacking parameters, and obtains the stacking efficiency optimization index; The environmental control module performs a time series analysis on the usage data of the humidity card and the desiccant based on the stacking efficiency optimization index to evaluate the impact on the PCB packaging environment. Based on the impact analysis results, the module adjusts the placement mechanism of the humidity card and the desiccant to obtain environmental control parameters. The robot optimization module analyzes the operating data of the six-axis robot and the three-axis robot based on the environmental control parameters, uses the CNN network to identify operational efficiency issues in the PCB packaging process, and monitors and adjusts the robot control parameters to generate an efficiency-optimized configuration. The thermal parameter setting module performs regression analysis on the heat shrink packaging temperature data and the heat shrink packaging pressure data based on the efficiency optimization configuration to determine the optimal thermal parameters; The monitoring and recording module applies the optimal thermal parameters to the packaging line, monitors and adjusts the packaging process of the PCB board in real time, and obtains a production data analysis log.

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