Design of Intelligent Integration System for Multi-source Industrial Field Data Based on Machine Learning

Aiming at the problem that information is scattered and difficult to manage in the current integration system, a multi-source industrial field data intelligent integration system based on machine learning is designed. The multi-source industrial field data synchronization device is designed, and the middleware technology is used to realize the integration of the field database, so as to realize the transparent access of the user to the field data source. Using machine learning-based host technology to integrate on-site data, design an intelligent retrieval engine for on-site data, and provide an integrated environment for users’ data processing. Design data integration channel point-to-point circuits, independently select power lines, remove impulse noise, and facilitate visual data integration. Use machine learning methods to train weight parameters and build an integrated task scheduling model to minimize construction queuing to process extraction and operation and maintenance tasks. Adjust the data topology structure, according to the specific needs of multi-source industrial field data intelligent integration, use database connection pool technology to integrate field data, and check the integrity of the integrated data. It can be seen from the experimental results that the system integration effect is good.