| With the rapid development of information technology,the amount of data generated from various sources continues to increase,especially with the growth of data driven by emerging technologies such as big data and the Internet of Things(Io T).Extracting valuable information from such vast,fast,and continuous data streams poses numerous challenges.In recent years,the demand for real-time processing of massive,high-speed streaming data has grown stronger in various fields,and although deep learning techniques have achieved significant results in modeling and analysis tasks for Io T observational data,there has been insufficient exploration of data with "concept drift" characteristics.Effectively addressing the problem of "concept drift" in data streams remains a significant challenge.Furthermore,it is essential to meet the growing demand for real-time data processing in the Io T field in specific streaming data processing scenarios.Therefore,the research focus is on exploring and developing streaming computing architectures and systems with real-time and dynamic data processing and analysis capabilities.To achieve this goal,it is necessary to fully exploit the information value contained in the data,construct efficient,scalable,and reliable real-time data processing solutions to meet the growing demand for data processing.In response to the aforementioned challenges,this thesis focuses on two main aspects:(1)To address the issue of concept drift in Io T streaming data,this study combines online learning theory and deep learning algorithms to propose online deep learning algorithms ECNN and ODLVAE.In particular,the ODLVAE algorithm,with dynamic updating,cold start,and applicability to both stationary and non-stationary data streams,demonstrates strong uncertainty adaptation,adaptability,and flexibility,effectively addressing the concept drift problem in streaming data.Experiments were conducted on public datasets and datasets collected by the team,and the results show that the algorithm has good accuracy and performance.(2)In response to the issues of data storage,data disorder,and data duplication faced in streaming data processing in specific Io T business scenarios,corresponding solutions are proposed.Based on Kafka,Flink,and other streaming data processing frameworks,a series of solutions are proposed,including data storage optimization,disorder processing,and data deduplication,effectively improving the efficiency and accuracy of streaming data processing.A distributed streaming processing architecture and system supporting online learning solutions were designed for the aforementioned content.Relying on various big data technologies,this study constructs a distributed cluster and system that meets the requirements of real-time,fault tolerance,and high availability,ensuring the stability and reliability of streaming data processing.In addition,the framework demonstrates exceptional scalability and flexibility,adapting to the demands of various business scenarios. |
