Research And Design Of Distributed Data Platform For Quality Inspection

With the continuous development of my country's industrial Internet and industrial big data technology and the steady implementation of various strategic plans,in the field of industrial quality inspection,the quality inspection data platform,quality intelligent inspection technology and massive data processing technology have gradually become the research of the manufacturing industry.focus.At present,manufacturers still generally use manual methods to screen products for quality,which is inefficient.As the amount of production data continues to increase,data processing capacity has become a bottleneck for forecasting efficiency.The existing quality inspection expert system can improve the efficiency of quality screening to a certain extent,but the realization of this type of inspection system relies on the subjective experience of experts,and does not have the ability of data disaster tolerance,efficient calculation and model update and sharing.Aiming at the above problems,this paper proposes and implements a quality inspectionoriented distributed data platform,which improves data storage and data processing capabilities through a distributed architecture.Obtain production process data samples through sensors,use data mining and deep learning methods to complete quality inspection feature engineering,predictive model training and evaluation,and realize distributed intelligent quality inspection.The research work of this paper mainly includes the following aspects:(1)Design and implementation of distributed data acquisition and transmission system.For the injection molding data collection scenario,a data collection module is designed based on Flume,and the logic design of data monitoring and data placement is optimized.Based on Kafka as the core,the data transmission module is designed,and the data access and topic division strategies are optimized.(2)Design and implementation of distributed data processing system.For streaming and batch computing,different data processing models are implemented? based on the Lambda architecture,a data processing system that can efficiently process offline and real-time tasks in parallel is designed? Hadoop and Flink are used to implement offline and streaming computing respectively,and offline Statistical indicators.(3)Design and implementation of quality inspection algorithm service system.Use Rsetful and Java Web technology to realize the design and deployment of algorithm service system.Provide external services such as service invocation,remote quality inspection,data upload,data visualization,visualization of model evaluation indicators,model update,and model management.(4)Research on the data quality inspection model of injection molding process.Preprocess the collected data to complete feature engineering and model training.Aiming at the problem that XGBoost is difficult to fully utilize the time series data of injection-molded highfrequency sensors,an optimization method based on LF-XGBoost time series feature extraction is proposed,which improves the XGBoost model's ability to extract time series features.Compared with the single model,the ROC score is improved by 3.7%.The quality inspection distributed data platform can be used to solve the problems of data acquisition,data transmission,data processing and quality inspection in industrial production in discrete manufacturing.When used in injection molding manufacturing scenarios,it greatly reduces the time-consuming quality inspection and improves the accuracy of quality inspection.