Intelligent monitoring system and method applied to PCBA board processing flow

Abstract

The invention discloses an intelligent monitoring system and method applied to a PCBA board processing flow, and relates to the technical field of intelligent monitoring. The system comprises a historical data management module, a data analysis and deviation calculation module, a performance evaluation and mapping relation construction module, a real-time data acquisition and correlation analysis module and an abnormity prompt and decision support module. The historical data management module arranges the historical data of the sensor to obtain target historical data; the data analysis and deviation calculation module analyzes target historical data to obtain a deviation coefficient and a deviation coefficient trend curve; the performance evaluation and mapping relation construction module evaluates the performance of the sensor and constructs a mapping relation between a deviation coefficient trend curve and a performance evaluation index; the real-time data acquisition and association analysis module acquires real-time data and calculates the association degree between the real-time data and the target historical data segment; and the exception prompt and decision support module carries out corresponding processing according to the association result of the real-time data.

Description

Technical Field

[0001] The present invention relates to the field of intelligent monitoring technology, and in particular to an intelligent monitoring system and method applied to a PCBA board processing flow. Background Art

[0002] With the rapid development of electronic products, PCBA, as a core component of the electronic manufacturing industry, occupies a vital position in the production process of modern electronic products. The manufacturing process of PCBA usually includes printing, patching, welding, testing and other links, and the precision and process requirements of each link are very high. Therefore, ensuring the quality of each link, avoiding defects, and optimizing the production process have become important goals of the industry.

[0003] With the continuous development of industrial automation and intelligent technology, traditional manual monitoring methods are gradually replaced by intelligent monitoring systems. These intelligent monitoring systems can collect key parameters (such as temperature, pressure, humidity, solder joint position, etc.) in the production process in real time through sensors, and provide real-time feedback on the production status through data processing and analysis. However, in practical applications, the existing intelligent monitoring system for PCBA processing has the following problems: In the PCBA processing process, sensors are widely used to collect key physical quantity data (such as welding temperature, pressure, solder joint position, etc.). However, as the sensor usage time increases, the accuracy of the sensor may gradually decrease, and the aging problem of the sensor may become more obvious over time. This may lead to deviations and inaccuracies in the monitoring data. Existing systems usually use regular calibration or replacement of sensors to deal with sensor aging problems, but this method has certain limitations, such as frequent maintenance costs and operational complexity, and it is difficult to effectively restore the performance and correct errors of sensors in long-term use, further increasing the probability of false alarms and missed alarms. Summary of the invention

[0004] The object of the present invention is to provide an intelligent monitoring system and method for PCBA board processing to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: An intelligent monitoring method applied to a PCBA board processing process includes the following steps: Step S100. Obtain the historical data of the entire life cycle of the sensors in each processing link in the PCBA production line, as well as the corresponding event records; analyze the historical data of each sensor according to the event records, so as to filter out the target historical data; for each processing link, divide the corresponding target historical data into several data segments; Step S200. According to the division result of the target historical data, each data segment is compared and analyzed with the corresponding standard process data segment to obtain the deviation coefficient; for each processing link, the deviation coefficients corresponding to all data segments are summarized, and the deviation coefficient trend curve of the corresponding target historical data is obtained; Step S300. Based on the deviation coefficient trend curve of the target historical data, the performance of the sensor of the corresponding processing link is evaluated to obtain a performance evaluation index; combining the deviation coefficient trend curve of the target historical data of the corresponding processing link with the performance evaluation index of the sensor, a mapping relationship between the two is constructed; Step S400. Acquire the real-time data of the sensors of each processing link of the PCBA production line, analyze the correlation degree between the real-time data of each processing link and the corresponding data segment of the target historical data, so as to obtain the corresponding correlation degree coefficient; match the data segment of the target historical data according to the correlation degree coefficient, and output the corresponding prompt information based on the matching result.

[0006] Furthermore, step S100 includes: S101. For each sensor in each processing link, obtain the historical data and corresponding event records of its entire life cycle; the historical data refers to the measurement data of the sensor, and the event record refers to the calibration or fault repair record of the sensor; for each sensor, summarize the corresponding historical data set D and event record set E, and the historical data set D = {d1, d2, ..., dn}, where d1 represents the first data point, d2 represents the second data point, and so on, dn represents the nth data point, and n represents the number of data point numbers; event record set E = {e1, e2, ..., em}, where e1 represents the first event record, e2 represents the second event record, and similarly, em represents the mth event record, and m represents the number of event record numbers; S102. Obtain the time period T where the corresponding event record is located, and the time period T = [t1, t2], where t1 represents the starting point of the corresponding event record, and t2 represents the ending point of the corresponding event record; according to the time period T, find the historical data of the same time period and remove them, so as to obtain the target historical data; for the target historical data, divide the target historical data of continuous time points into a data segment, so as to obtain data segments of several continuous time points; according to the time period T where the event record is located, obtain the timestamp t0 corresponding to the last data point of each data segment, and calculate the difference between the timestamp t0 and the starting point of the time period where all the event records are located, and obtain the difference δt, and δt = t1-t0; if the difference δt is less than or equal to the threshold t, then Associate the data segment where timestamp t0 is located with the corresponding event record; according to the corresponding event category of the event record, classify all data segments into two categories: calibration and fault maintenance, which are represented as target historical data set DA and target historical data set DB respectively; and the target historical data set DA={Da1,Da2,...,Dax}, where Da1 represents the target historical data segment corresponding to the first calibration event, Da2 represents the target historical data segment corresponding to the second calibration event, and so on, Dax represents the target historical data segment corresponding to the xth calibration event; similarly, the target historical data set DB is analyzed in the same way as the target historical data set DA, and the data segments corresponding to the target historical data set DA and the target historical data set DB are arranged in chronological order.

[0007] Further, step S200 includes: S201. For the sensor of each processing link, define a standard process data set P, and P={p1,p2,...,pk}, where p1 represents the first data point in the standard process flow, p2 represents the second data point in the standard process flow, and so on, pk represents the kth data point in the standard process flow, and k represents the data point number in the standard process flow; the data points corresponding to the standard process data set are the values ​​under ideal conditions; for the target historical data of the sensor of each processing link, according to the corresponding classification results, the following analysis is performed on each data segment in the target historical data set DA and the target historical data set DB: for each data point di in the data segment Di and the corresponding standard process data point pj, where Di∈DA∪DB; calculate the corresponding deviation coefficient α, and the specific calculation formula is: α=[|di-(di+1)| / |pj-(pj+1)|]×|di-pj|; S202. Summarize the deviation coefficient α of the data point corresponding to each data segment in the target historical data set DA and the target historical data set DB, and draw a deviation coefficient trend curve in a plane rectangular coordinate system for each data segment in chronological order, and the horizontal axis of the plane rectangular coordinate system represents the data point number i, and the vertical axis represents the deviation coefficient α; thereby obtaining the deviation coefficient trend curve LDa and the deviation coefficient trend curve LDb corresponding to each data segment in the target historical data set DA and the target historical data set DB.

[0008] Furthermore, step S300 includes: S301. For the sensors in each processing link, the same analysis is performed according to the deviation coefficient trend curve LDa and the deviation coefficient trend curve LDb corresponding to each data segment in the target historical data set DA and the target historical data set DB. The specific analysis process for the deviation coefficient trend curve LDa corresponding to each data segment in the target historical data set DA is as follows: The maximum value of the deviation coefficient α corresponding to each data segment in the target historical data set DA is extracted and the average value μa and the standard deviation σa are calculated, and the deviation coefficient threshold Ta corresponding to the calibration event is obtained, and Ta=μa-g×σa, where g represents a constant; combined with the deviation coefficient trend curve LDa corresponding to each data segment in the target historical data set DA, the performance evaluation index Ra of the corresponding sensor is calculated, and the specific calculation formula is: Ra=(1 / N)×∑u∈[1,N],Ku×[1-(αu / Ta)], Where Ku represents the slope of the u-th data point of the deviation coefficient trend curve LDa corresponding to the data segment, αu represents the deviation coefficient of the u-th data point of the deviation coefficient trend curve LDa corresponding to the data segment, and N represents the total number of data points of the deviation coefficient trend curve LDa corresponding to the data segment; Similarly, referring to the above analysis process of the deviation coefficient trend curve LDa corresponding to each data segment in the target historical data set DA, the deviation coefficient threshold Tb corresponding to the fault maintenance event and the performance evaluation index Rb of the corresponding sensor are obtained; S302. For each data segment in the target historical data set DA and the target historical data set DB, the mapping relationship between the deviation coefficient trend curve LDa and the deviation coefficient trend curve LDb and the performance evaluation index Ra and the performance evaluation index Rb is constructed respectively, and they are respectively expressed as: Ga(LDa, Ra) and Gb(LDb, Rb).

[0009] Furthermore, step S400 includes: S401. Obtain the real-time data of sensors in each processing link of the PCBA production line. For each sensor's real-time data Dr, perform correlation analysis with each data segment corresponding to the corresponding target historical data set DA and the target historical data set DB. The specific analysis process is as follows: Calculate the distance metric dist(Dr,Di) between the real-time data Dr and the corresponding target historical data set DA and each data segment Di in the target historical data set DB, and dist(Dr,Di)=[∑v∈[1,V],(rv-dv)2]^(1 / 2), where V represents the total number of data points of the real-time data, rv represents the vth data point of the real-time data Dr, and dv represents the vth data point of the data segment Di; calculate the correlation coefficient P according to the distance metric dist(Dr,Di), and P=1 / [1+dist(Dr,Di)]; S402. Summarize the correlation coefficient P between the real-time data Dr and the corresponding target historical data set DA and each data segment Di in the target historical data set DB, select the data segment Di with the largest correlation coefficient P as the matching result of the current real-time data Dr; and obtain the value of the corresponding correlation coefficient P. If the correlation coefficient P is less than the threshold value P0, no notification information is output; If the correlation coefficient P is greater than or equal to the threshold value P0, then according to the data segment Di corresponding to the matching result, the mapping relationship between the corresponding deviation coefficient trend curve and the performance evaluation index is obtained, and the deviation coefficient trend curve Lr corresponding to the real-time data is drawn in combination with the analysis method of historical data, and any point on the deviation coefficient trend curve of the data segment Di is taken as the origin, and the starting point of the deviation coefficient trend curve Lr is overlapped with the origin, so that the deviation coefficient trend curve Lr slides on the data points on the deviation coefficient trend curve of the data segment Di in sequence, and stops when the data points that overlap between the two are the most, and the time span T0 between the last overlapping data point and the end point of the data segment Di is calculated, and the time span T0 and the event number corresponding to the data segment Di and the performance evaluation index are output to the relevant personnel, and the relevant personnel perform further processing. Among them, the time span T0 is used as the predicted time of the calibration event or the fault maintenance event. According to the output time span T0, the relevant personnel perform corresponding processing based on the predicted time of the calibration event or the fault maintenance event; the performance evaluation index is used as a reference basis for subsequent feedback to help the relevant personnel make timely decisions and adjustments.

[0010] An intelligent monitoring system applied to PCBA board processing flow, the system includes: historical data management module, data analysis and deviation calculation module, performance evaluation and mapping relationship construction module, real-time data collection and correlation analysis module, and abnormal prompt and decision support module; The historical data management module obtains the full life cycle data of sensors in each processing link of the PCBA production line, including measurement data and event records; organizes and classifies the historical data of sensors, builds historical data sets, and manages event records; analyzes the historical data for time periods, removes the target historical data after the event, and processes the target historical data in segments; The data analysis and deviation calculation module compares the target historical data set with the standard process data, calculates the deviation coefficient of each data segment, and summarizes and generates a deviation coefficient trend curve of the target historical data; The performance evaluation and mapping relationship building module evaluates the sensor performance based on the deviation coefficient trend curve of the target historical data, calculates the performance evaluation index, and builds the mapping relationship between the deviation coefficient trend curve and the performance evaluation index; The real-time data acquisition and correlation analysis module obtains the real-time data of sensors in each processing link of the PCBA production line and performs preprocessing; by calculating the correlation degree between the real-time data and the target historical data segment, the correlation coefficient between the real-time data and the target historical data segment is calculated based on the distance measurement, the correlation degree between the real-time data and the historical data segment is summarized, and the data segment with the largest correlation degree is selected for real-time matching; The abnormal prompt and decision support module performs corresponding processing based on the correlation results of real-time data.

[0011] Further, the historical data management module includes a data acquisition unit, a data preprocessing unit and an event association unit; The data acquisition unit obtains the historical data and event records of the entire life cycle of the sensors in each processing link; the data preprocessing unit cleans and preliminarily processes the collected historical data; the event association unit associates the historical data with the corresponding event records according to the time period of the event records, and classifies them into calibration event data sets and fault maintenance event data sets.

[0012] Further, the data analysis and deviation calculation module includes a data segmentation unit, a deviation calculation unit and a trend curve drawing unit; The data segmentation unit divides the target historical data into several continuous time periods according to the event recording time period, and marks and classifies each data segment; the deviation calculation unit calculates the deviation coefficient between each data point and the standard process data point for each data segment; the trend curve drawing unit draws the deviation coefficient trend curve of each data segment according to the calculated deviation coefficient, thereby obtaining the deviation coefficient trend curve of the target historical data sets DA and DB.

[0013] Further, the performance evaluation and mapping relationship construction module includes a threshold calculation unit, a performance evaluation unit and a mapping relationship construction unit; The threshold calculation unit calculates the deviation coefficient threshold of calibration and fault repair events; the performance evaluation unit calculates the performance evaluation index of each sensor based on the deviation coefficient trend curve; the mapping relationship construction unit establishes a mapping relationship based on the deviation coefficient trend curve and the performance evaluation index of each data segment.

[0014] Furthermore, the real-time data collection and correlation analysis module includes a real-time data collection unit, a data matching unit and a correlation analysis unit; The real-time data acquisition unit obtains the real-time data of sensors in each processing link of the PCBA production line; the data matching unit calculates the distance measurement between the real-time data and each data segment in the target historical data set DA and DB, and calculates the corresponding correlation coefficient based on the distance measurement of the data segment; the correlation analysis unit selects the historical data segment with the highest matching degree with the real-time data based on the correlation coefficient, and compares and analyzes the trend curves of the real-time data and the matching historical data segments.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: through in-depth analysis of historical data and event records of the entire life cycle of the sensor, the present invention can better understand and evaluate the performance degradation trend of the sensor, avoiding the simple processing method of relying only on regular calibration or replacement of the sensor in the traditional method; by establishing the association between the sensor historical data and the event record, the system can accurately track the performance changes of the sensor, and make targeted corrections or adjustments, thereby reducing the measurement errors caused by sensor aging. In each processing link, the trend curve of the sensor's deviation coefficient can be generated by combining the comparison analysis of the target historical data and the standard process data segment; this trend curve not only helps to identify the performance changes of the sensor, but also can predict potential problems in the processing process at the data segment level, avoiding the limitations of the traditional method of simply relying on instantaneous data or static thresholds to judge whether the equipment is normal. By analyzing the correlation between real-time data and target historical data segments, the present invention can evaluate the working status of the sensor in real time based on the matching results of real-time monitoring data and historical data; this method can effectively cope with the dynamic changes in complex production environments, help discover potential abnormalities or failure risks, and issue prompts or warnings in time, greatly reducing the probability of missed reports or false reports. By combining the deviation coefficient trend curve of historical data with the performance evaluation index of the sensor, the present invention can more comprehensively reflect the comprehensive performance of the sensor; and by establishing a mapping relationship between the deviation coefficient trend curve and the performance evaluation index, the system can more accurately perform performance evaluation of real-time data, further improving the reliability and stability of the production process. Compared with the traditional method of regularly calibrating or replacing sensors, the present invention provides a data-driven dynamic monitoring and early warning mechanism that can intervene before the sensor performance drops to a critical value; this monitoring method based on data analysis can not only improve the accuracy and reliability of sensor use, but also effectively reduce the high maintenance costs caused by frequent calibration or replacement of sensors, thereby improving production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 It is a schematic diagram of an intelligent monitoring system module applied to a PCBA board processing flow of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] See also Figure 1 , the present invention provides a technical solution: An intelligent monitoring system applied to PCBA board processing flow, the system includes: historical data management module, data analysis and deviation calculation module, performance evaluation and mapping relationship construction module, real-time data collection and correlation analysis module, and abnormal prompt and decision support module; The historical data management module obtains the full life cycle data of sensors in each processing link of the PCBA production line, including measurement data and event records; organizes and classifies the historical data of sensors, builds historical data sets, and manages event records; analyzes the historical data for time periods, removes the target historical data after the event, and processes the target historical data in segments; The data analysis and deviation calculation module compares the target historical data set with the standard process data, calculates the deviation coefficient of each data segment, and summarizes and generates a deviation coefficient trend curve of the target historical data; The performance evaluation and mapping relationship building module evaluates the sensor performance based on the deviation coefficient trend curve of the target historical data, calculates the performance evaluation index, and builds the mapping relationship between the deviation coefficient trend curve and the performance evaluation index; The real-time data acquisition and correlation analysis module obtains the real-time data of sensors in each processing link of the PCBA production line and performs preprocessing; by calculating the correlation degree between the real-time data and the target historical data segment, the correlation coefficient between the real-time data and the target historical data segment is calculated based on the distance measurement, the correlation degree between the real-time data and the historical data segment is summarized, and the data segment with the largest correlation degree is selected for real-time matching; The abnormal prompt and decision support module performs corresponding processing based on the correlation results of real-time data.

[0019] The historical data management module includes a data acquisition unit, a data preprocessing unit and an event association unit; The data acquisition unit obtains the historical data and event records of the entire life cycle of the sensors in each processing link; the data preprocessing unit cleans and preliminarily processes the collected historical data; the event association unit associates the historical data with the corresponding event records according to the time period of the event records, and classifies them into calibration event data sets and fault maintenance event data sets.

[0020] The data analysis and deviation calculation module includes a data segmentation unit, a deviation calculation unit and a trend curve drawing unit; The data segmentation unit divides the target historical data into several continuous time periods according to the event recording time period, and marks and classifies each data segment; the deviation calculation unit calculates the deviation coefficient between each data point and the standard process data point for each data segment; the trend curve drawing unit draws the deviation coefficient trend curve of each data segment according to the calculated deviation coefficient, thereby obtaining the deviation coefficient trend curve of the target historical data sets DA and DB.

[0021] The performance evaluation and mapping relationship building module includes a threshold calculation unit, a performance evaluation unit and a mapping relationship building unit; The threshold calculation unit calculates the deviation coefficient threshold of calibration and fault repair events; the performance evaluation unit calculates the performance evaluation index of each sensor based on the deviation coefficient trend curve; the mapping relationship construction unit establishes a mapping relationship based on the deviation coefficient trend curve and the performance evaluation index of each data segment.

[0022] The real-time data collection and correlation analysis module includes a real-time data collection unit, a data matching unit and a correlation analysis unit; The real-time data acquisition unit obtains the real-time data of sensors in each processing link of the PCBA production line; the data matching unit calculates the distance measurement between the real-time data and each data segment in the target historical data set DA and DB, and calculates the corresponding correlation coefficient based on the distance measurement of the data segment; the correlation analysis unit selects the historical data segment with the highest matching degree with the real-time data based on the correlation coefficient, and compares and analyzes the trend curves of the real-time data and the matching historical data segments.

[0023] An intelligent monitoring method applied to a PCBA board processing process includes the following steps: Step S100. Obtain the historical data of the entire life cycle of the sensors in each processing link in the PCBA production line, as well as the corresponding event records; analyze the historical data of each sensor according to the event records, so as to filter out the target historical data; for each processing link, divide the corresponding target historical data into several data segments; Step S200. According to the division result of the target historical data, each data segment is compared and analyzed with the corresponding standard process data segment to obtain the deviation coefficient; for each processing link, the deviation coefficients corresponding to all data segments are summarized, and the deviation coefficient trend curve of the corresponding target historical data is obtained; Step S300. Based on the deviation coefficient trend curve of the target historical data, the performance of the sensor of the corresponding processing link is evaluated to obtain a performance evaluation index; combining the deviation coefficient trend curve of the target historical data of the corresponding processing link with the performance evaluation index of the sensor, a mapping relationship between the two is constructed; Step S400. Acquire the real-time data of the sensors of each processing link of the PCBA production line, analyze the correlation degree between the real-time data of each processing link and the corresponding data segment of the target historical data, so as to obtain the corresponding correlation degree coefficient; match the data segment of the target historical data according to the correlation degree coefficient, and output the corresponding prompt information based on the matching result.

[0024] Step S100 includes: S101. For each sensor in each processing link, obtain the historical data and corresponding event records of its entire life cycle; the historical data refers to the measurement data of the sensor, and the event record refers to the calibration or fault repair record of the sensor; for each sensor, summarize the corresponding historical data set D and event record set E, and the historical data set D = {d1, d2, ..., dn}, where d1 represents the first data point, d2 represents the second data point, and so on, dn represents the nth data point, and n represents the number of data point numbers; event record set E = {e1, e2, ..., em}, where e1 represents the first event record, e2 represents the second event record, and similarly, em represents the mth event record, and m represents the number of event record numbers; S102. Obtain the time period T where the corresponding event record is located, and the time period T = [t1, t2], where t1 represents the starting point of the corresponding event record, and t2 represents the ending point of the corresponding event record; according to the time period T, find the historical data of the same time period and remove them, so as to obtain the target historical data; for the target historical data, divide the target historical data of continuous time points into a data segment, so as to obtain data segments of several continuous time points; according to the time period T where the event record is located, obtain the timestamp t0 corresponding to the last data point of each data segment, and calculate the difference between the timestamp t0 and the starting point of the time period where all the event records are located, and obtain the difference δt, and δt = t1-t0; if the difference δt is less than or equal to the threshold t, then Associate the data segment where timestamp t0 is located with the corresponding event record; according to the corresponding event category of the event record, classify all data segments into two categories: calibration and fault maintenance, which are represented as target historical data set DA and target historical data set DB respectively; and the target historical data set DA={Da1,Da2,...,Dax}, where Da1 represents the target historical data segment corresponding to the first calibration event, Da2 represents the target historical data segment corresponding to the second calibration event, and so on, Dax represents the target historical data segment corresponding to the xth calibration event; similarly, the target historical data set DB is analyzed in the same way as the target historical data set DA, and the data segments corresponding to the target historical data set DA and the target historical data set DB are arranged in chronological order.

[0025] Step S200 includes: S201. For the sensor of each processing link, define a standard process data set P, and P={p1,p2,...,pk}, where p1 represents the first data point in the standard process flow, p2 represents the second data point in the standard process flow, and so on, pk represents the kth data point in the standard process flow, and k represents the data point number in the standard process flow; the data points corresponding to the standard process data set are the values ​​under ideal conditions; for the target historical data of the sensor of each processing link, according to the corresponding classification results, the following analysis is performed on each data segment in the target historical data set DA and the target historical data set DB: for each data point di in the data segment Di and the corresponding standard process data point pj, where Di∈DA∪DB; calculate the corresponding deviation coefficient α, and the specific calculation formula is: α=[|di-(di+1)| / |pj-(pj+1)|]×|di-pj|; S202. Summarize the deviation coefficient α of the data point corresponding to each data segment in the target historical data set DA and the target historical data set DB, and draw a deviation coefficient trend curve in a plane rectangular coordinate system for each data segment in chronological order, and the horizontal axis of the plane rectangular coordinate system represents the data point number i, and the vertical axis represents the deviation coefficient α; thereby obtaining the deviation coefficient trend curve LDa and the deviation coefficient trend curve LDb corresponding to each data segment in the target historical data set DA and the target historical data set DB.

[0026] Step S300 includes: S301. For the sensors in each processing link, the same analysis is performed according to the deviation coefficient trend curve LDa and the deviation coefficient trend curve LDb corresponding to each data segment in the target historical data set DA and the target historical data set DB. The specific analysis process for the deviation coefficient trend curve LDa corresponding to each data segment in the target historical data set DA is as follows: The maximum value of the deviation coefficient α corresponding to each data segment in the target historical data set DA is extracted and the average value μa and the standard deviation σa are calculated, and the deviation coefficient threshold Ta corresponding to the calibration event is obtained, and Ta=μa-g×σa, where g represents a constant; combined with the deviation coefficient trend curve LDa corresponding to each data segment in the target historical data set DA, the performance evaluation index Ra of the corresponding sensor is calculated, and the specific calculation formula is: Ra=(1 / N)×∑u∈[1,N],Ku×[1-(αu / Ta)], Where Ku represents the slope of the u-th data point of the deviation coefficient trend curve LDa corresponding to the data segment, αu represents the deviation coefficient of the u-th data point of the deviation coefficient trend curve LDa corresponding to the data segment, and N represents the total number of data points of the deviation coefficient trend curve LDa corresponding to the data segment; Similarly, referring to the above analysis process of the deviation coefficient trend curve LDa corresponding to each data segment in the target historical data set DA, the deviation coefficient threshold Tb corresponding to the fault maintenance event and the performance evaluation index Rb of the corresponding sensor are obtained; S302. For each data segment in the target historical data set DA and the target historical data set DB, the mapping relationship between the deviation coefficient trend curve LDa and the deviation coefficient trend curve LDb and the performance evaluation index Ra and the performance evaluation index Rb is constructed respectively, and they are respectively expressed as: Ga(LDa, Ra) and Gb(LDb, Rb).

[0027] Step S400 includes: S401. Obtain the real-time data of sensors in each processing link of the PCBA production line. For each sensor's real-time data Dr, perform correlation analysis with each data segment corresponding to the corresponding target historical data set DA and the target historical data set DB. The specific analysis process is as follows: Calculate the distance metric dist(Dr,Di) between the real-time data Dr and the corresponding target historical data set DA and each data segment Di in the target historical data set DB, and dist(Dr,Di)=[∑v∈[1,V],(rv-dv) 2 ]^(1 / 2), where V represents the total number of data points of real-time data, rv represents the vth data point of real-time data Dr, and dv represents the vth data point of data segment Di; according to the distance metric dist(Dr,Di), the correlation coefficient P is calculated, and P=1 / [1+dist(Dr,Di)]; S402. Summarize the correlation coefficient P between the real-time data Dr and the corresponding target historical data set DA and each data segment Di in the target historical data set DB, select the data segment Di with the largest correlation coefficient P as the matching result of the current real-time data Dr; and obtain the value of the corresponding correlation coefficient P. If the correlation coefficient P is less than the threshold value P0, no notification information is output; If the correlation coefficient P is greater than or equal to the threshold value P0, then according to the data segment Di corresponding to the matching result, the mapping relationship between the corresponding deviation coefficient trend curve and the performance evaluation index is obtained, and the deviation coefficient trend curve Lr corresponding to the real-time data is drawn in combination with the analysis method of historical data, and any point on the deviation coefficient trend curve of the data segment Di is taken as the origin, and the starting point of the deviation coefficient trend curve Lr is overlapped with the origin, so that the deviation coefficient trend curve Lr slides on the data points on the deviation coefficient trend curve of the data segment Di in sequence, and stops when the data points that overlap between the two are the most, and the time span T0 between the last overlapping data point and the end point of the data segment Di is calculated, and the time span T0 and the event number corresponding to the data segment Di and the performance evaluation index are output to the relevant personnel, and the relevant personnel perform further processing. Among them, the time span T0 is used as the predicted time of the calibration event or the fault maintenance event. According to the output time span T0, the relevant personnel perform corresponding processing based on the predicted time of the calibration event or the fault maintenance event; the performance evaluation index is used as a reference basis for subsequent feedback to help the relevant personnel make timely decisions and adjustments.

[0028] In this embodiment, the best matching historical data segment is specifically: For each data segment Di in the real-time data Dr and historical data sets DA and DB, calculate the correlation coefficient P of all data segments; select the historical data segment Di with the largest correlation coefficient P as the best data segment matching the real-time data Dr; if the maximum correlation coefficient P is less than the preset threshold value P0, it is considered that the correlation between the real-time data and the historical data is low, and no notification information is output. If the maximum correlation coefficient P is greater than or equal to the threshold value P0, proceed to the subsequent analysis steps; obtain the deviation coefficient trend curve corresponding to the selected historical data segment Di, slide the deviation coefficient trend curve Lr on the deviation coefficient trend curve Li of the historical data segment Di, select a starting point, and make it coincide with the deviation coefficient trend curve of the historical data segment, and make the number of data points that Lr and Li overlap as much as possible through sliding, and stop sliding when the number of data points that overlap between the two is the largest; calculate the time span T0 between the last overlapping data point and the end point of the data segment Di; Assume that on a PCBA production line, the real-time data monitored by a sensor corresponds to temperature changes. Matching the historical data segment Di with the deviation coefficient trend curve Lr of the real-time data yields the following results: When the number of data points that overlap between the deviation coefficient trend curve Lr and the deviation coefficient trend curve Li of the historical data segment Di is the largest, the historical data segment Di is a calibration event, and the time span T0 between the last overlapping data point and the end point of the data segment Di is calculated. Assuming that the time span T0 is 4 hours, it means that the calibration event is expected to occur 4 hours later. Through analysis, the performance evaluation index Ra is obtained, and the output time span T0 is 4 hours, and the performance evaluation index Ra is given to relevant personnel, who then perform corresponding processing.

[0029] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0030] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An intelligent monitoring method applied to PCBA board processing flow, characterized by: The method comprises the following steps: Step S100. Obtain the historical data of the entire life cycle of the sensors in each processing link in the PCBA production line, as well as the corresponding event records; analyze the historical data of each sensor according to the event records, so as to filter out the target historical data; for each processing link, divide the corresponding target historical data into several data segments; Step S200. According to the division result of the target historical data, each data segment is compared and analyzed with the corresponding standard process data segment to obtain the deviation coefficient; for each processing link, the deviation coefficients corresponding to all data segments are summarized, and the deviation coefficient trend curve of the corresponding target historical data is obtained; Step S300. Based on the deviation coefficient trend curve of the target historical data, the performance of the sensor of the corresponding processing link is evaluated to obtain a performance evaluation index; combining the deviation coefficient trend curve of the target historical data of the corresponding processing link with the performance evaluation index of the sensor, a mapping relationship between the two is constructed; Step S400. Acquire the real-time data of the sensors of each processing link of the PCBA production line, analyze the correlation degree between the real-time data of each processing link and the corresponding data segment of the target historical data, so as to obtain the corresponding correlation degree coefficient; match the data segment of the target historical data according to the correlation degree coefficient, and output the corresponding prompt information based on the matching result.

2. The intelligent monitoring method for PCBA board processing according to claim 1 is characterized in that: The step S100 includes: S101. For each sensor in each processing link, obtain the historical data and corresponding event records of its entire life cycle; the historical data refers to the measurement data of the sensor, and the event record refers to the calibration or fault repair record of the sensor; for each sensor, summarize the corresponding historical data set D and event record set E, and the historical data set D = {d1, d2, ..., dn}, where d1 represents the first data point, d2 represents the second data point, and so on, dn represents the nth data point, and n represents the number of data point numbers; event record set E = {e1, e2, ..., em}, where e1 represents the first event record, e2 represents the second event record, and similarly, em represents the mth event record, and m represents the number of event record numbers; S102. Obtain the time period T where the corresponding event record is located, and the time period T = [t1, t2], where t1 represents the starting point of the corresponding event record, and t2 represents the ending point of the corresponding event record; according to the time period T, find the historical data of the same time period and remove them, so as to obtain the target historical data; for the target historical data, divide the target historical data of continuous time points into a data segment, so as to obtain data segments of several continuous time points; according to the time period T where the event record is located, obtain the timestamp t0 corresponding to the last data point of each data segment, and calculate the difference between the timestamp t0 and the starting point of the time period where all the event records are located, and obtain the difference δt, and δt = t1-t0; if the difference δt is less than or equal to the threshold t, then Associate the data segment where timestamp t0 is located with the corresponding event record; according to the corresponding event category of the event record, classify all data segments into two categories: calibration and fault maintenance, which are represented as target historical data set DA and target historical data set DB respectively; and the target historical data set DA={Da1,Da2,...,Dax}, where Da1 represents the target historical data segment corresponding to the first calibration event, Da2 represents the target historical data segment corresponding to the second calibration event, and so on, Dax represents the target historical data segment corresponding to the xth calibration event; similarly, the target historical data set DB is analyzed in the same way as the target historical data set DA, and the data segments corresponding to the target historical data set DA and the target historical data set DB are arranged in chronological order.

3. The intelligent monitoring method for PCBA board processing according to claim 2 is characterized in that: The step S200 includes: S201. For the sensors of each processing link, define a standard process data set P, and P={p1,p2,...,pk}, where p1 represents the first data point in the standard process flow, p2 represents the second data point in the standard process flow, and so on, pk represents the kth data point in the standard process flow, and k represents the data point number in the standard process flow; for the target historical data of the sensors of each processing link, according to the corresponding classification results, for each data segment in the target historical data set DA and the target historical data set DB, the following analysis is performed: for each data point di in the data segment Di and the corresponding standard process data point pj, where Di∈DA∪DB; calculate the corresponding deviation coefficient α, and the specific calculation formula is: α=[|di-(di+1)| / |pj-(pj+1)|]×|di-pj|; S202. Summarize the deviation coefficient α of the data point corresponding to each data segment in the target historical data set DA and the target historical data set DB, and draw a deviation coefficient trend curve in a plane rectangular coordinate system for each data segment in chronological order, and the horizontal axis of the plane rectangular coordinate system represents the data point number i, and the vertical axis represents the deviation coefficient α; thereby obtaining the deviation coefficient trend curve LDa and the deviation coefficient trend curve LDb corresponding to each data segment in the target historical data set DA and the target historical data set DB.

4. The intelligent monitoring method for PCBA board processing according to claim 3 is characterized in that: The step S300 includes: S301. For the sensors in each processing link, the same analysis is performed according to the deviation coefficient trend curve LDa and the deviation coefficient trend curve LDb corresponding to each data segment in the target historical data set DA and the target historical data set DB. The specific analysis process for the deviation coefficient trend curve LDa corresponding to each data segment in the target historical data set DA is as follows: The maximum value of the deviation coefficient α corresponding to each data segment in the target historical data set DA is extracted and the average value μa and the standard deviation σa are calculated, and the deviation coefficient threshold Ta corresponding to the calibration event is obtained, and Ta=μa-g×σa, where g represents a constant; combined with the deviation coefficient trend curve LDa corresponding to each data segment in the target historical data set DA, the performance evaluation index Ra of the corresponding sensor is calculated, and the specific calculation formula is: Ra=(1 / N)×∑u∈[1,N],Ku×[1-(αu / Ta)], Where Ku represents the slope of the u-th data point of the deviation coefficient trend curve LDa corresponding to the data segment, αu represents the deviation coefficient of the u-th data point of the deviation coefficient trend curve LDa corresponding to the data segment, and N represents the total number of data points of the deviation coefficient trend curve LDa corresponding to the data segment; Similarly, referring to the above analysis process of the deviation coefficient trend curve LDa corresponding to each data segment in the target historical data set DA, the deviation coefficient threshold Tb corresponding to the fault maintenance event and the performance evaluation index Rb of the corresponding sensor are obtained; S302. For each data segment in the target historical data set DA and the target historical data set DB, the mapping relationship between the deviation coefficient trend curve LDa and the deviation coefficient trend curve LDb and the performance evaluation index Ra and the performance evaluation index Rb is constructed respectively, and they are respectively expressed as: Ga(LDa, Ra) and Gb(LDb, Rb).

5. The intelligent monitoring method for PCBA board processing according to claim 4 is characterized in that: The step S400 includes: S401. Obtain the real-time data of sensors in each processing link of the PCBA production line. For each sensor's real-time data Dr, perform correlation analysis with each data segment corresponding to the corresponding target historical data set DA and the target historical data set DB. The specific analysis process is as follows: Calculate the distance metric dist(Dr,Di) between the real-time data Dr and the corresponding target historical data set DA and each data segment Di in the target historical data set DB, and dist(Dr,Di)=[∑v∈[1,V],(rv-dv) 2 ]^(1 / 2), where V represents the total number of data points of real-time data, rv represents the vth data point of real-time data Dr, and dv represents the vth data point of data segment Di; according to the distance metric dist(Dr,Di), the correlation coefficient P is calculated, and P=1 / [1+dist(Dr,Di)]; S402. Summarize the correlation coefficient P between the real-time data Dr and the corresponding target historical data set DA and each data segment Di in the target historical data set DB, select the data segment Di with the largest correlation coefficient P as the matching result of the current real-time data Dr; and obtain the value of the corresponding correlation coefficient P. If the correlation coefficient P is less than the threshold value P0, no notification information is output; If the correlation coefficient P is greater than or equal to the threshold value P0, then according to the data segment Di corresponding to the matching result, the mapping relationship between the corresponding deviation coefficient trend curve and the performance evaluation index is obtained, and combined with the analysis method of historical data, the deviation coefficient trend curve Lr corresponding to the real-time data is drawn, and any point on the deviation coefficient trend curve of the data segment Di is taken as the origin, and the starting point of the deviation coefficient trend curve Lr is overlapped with the origin, so that the deviation coefficient trend curve Lr slides on the data points on the deviation coefficient trend curve of the data segment Di in sequence, and stops when the number of data points overlapping between the two is the largest, and calculates the time span T0 between the last overlapping data point and the end point of the data segment Di, and outputs the time span T0 and the event number corresponding to the data segment Di and the performance evaluation index to the relevant personnel, who will perform further processing.

6. An intelligent monitoring system for PCBA board processing flow, applied to an intelligent monitoring method for PCBA board processing flow according to any one of claims 1 to 5, characterized in that: The system includes: a historical data management module, a data analysis and deviation calculation module, a performance evaluation and mapping relationship construction module, a real-time data collection and association analysis module, and an abnormality prompt and decision support module; The historical data management module obtains the full life cycle data of sensors in each processing link of the PCBA production line, including measurement data and event records; organizes and classifies the historical data of the sensors, constructs a historical data set, and manages the event records; analyzes the historical data for a certain period of time, removes the target historical data after the event, and processes the target historical data in sections; The data analysis and deviation calculation module compares the target historical data set with the standard process data, calculates the deviation coefficient of each data segment, and summarizes and generates a deviation coefficient trend curve of the target historical data; The performance evaluation and mapping relationship building module evaluates the sensor performance based on the deviation coefficient trend curve of the target historical data, calculates the performance evaluation index, and builds a mapping relationship between the deviation coefficient trend curve and the performance evaluation index; The real-time data acquisition and association analysis module obtains the real-time data of the sensors in each processing link of the PCBA production line and performs preprocessing; by calculating the correlation degree between the real-time data and the target historical data segment, the correlation degree coefficient between the real-time data and the target historical data segment is calculated based on the distance metric, the correlation degree between the real-time data and the historical data segment is summarized, and the data segment with the largest correlation degree is selected for real-time matching; The abnormal prompt and decision support module performs corresponding processing according to the correlation result of the real-time data.

7. The intelligent monitoring system for PCBA board processing according to claim 6 is characterized in that: The historical data management module includes a data acquisition unit, a data preprocessing unit and an event association unit; The data acquisition unit acquires historical data and event records of the entire life cycle of sensors in each processing link; the data preprocessing unit cleans and preliminarily processes the collected historical data; The event association unit associates the historical data with the corresponding event records according to the time period of the event records, and classifies the historical data into a calibration event data set and a fault repair event data set.

8. The intelligent monitoring system for PCBA board processing according to claim 6 is characterized in that: The data analysis and deviation calculation module includes a data segmentation unit, a deviation calculation unit and a trend curve drawing unit; The data segmentation unit divides the target historical data into a number of continuous time periods according to the event recording time period, and marks and classifies each data segment; The deviation calculation unit calculates the deviation coefficient of each data point and the standard process data point for each data segment; the trend curve drawing unit draws the deviation coefficient trend curve of each data segment according to the calculated deviation coefficient, thereby obtaining the deviation coefficient trend curve of the target historical data sets DA and DB.

9. The intelligent monitoring system for PCBA board processing according to claim 6 is characterized in that: The performance evaluation and mapping relationship building module includes a threshold calculation unit, a performance evaluation unit and a mapping relationship building unit; The threshold calculation unit calculates the deviation coefficient threshold of the calibration and fault repair events; the performance evaluation unit calculates the performance evaluation index of each sensor based on the deviation coefficient trend curve; The mapping relationship building unit establishes a mapping relationship according to the deviation coefficient trend curve and the performance evaluation index of each data segment.

10. The intelligent monitoring system for PCBA board processing according to claim 6 is characterized in that: The real-time data collection and association analysis module includes a real-time data collection unit, a data matching unit and an association analysis unit; The real-time data acquisition unit acquires the real-time data of sensors in each processing link of the PCBA production line; the data matching unit calculates the distance metric between the real-time data and each data segment in the target historical data set DA and DB, and calculates the corresponding correlation degree coefficient according to the distance metric of the data segment; The association analysis unit selects the historical data segment with the highest matching degree with the real-time data based on the correlation degree coefficient, and compares and analyzes the trend curves of the real-time data and the matching historical data segment.

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