面向流式数据的异常检测系统

he anomaly detection system plays a crucial role in various stages of enterprise production. This paper is dedicated to introducing an engineered implementation of a streaming data anomaly detection system, designed to efficiently handle large-scale data streams in real-time computing environments, ensuring key functionalities such as high concurrency and seamless scalability. Initially, we discuss the growing significance of streaming data processing, particularly in scenarios requiring real-time response and monitoring. Traditional batch processing systems fall short of meeting real-time requirements, making streaming data processing systems pivotal for handling large-scale data streams. However, the engineering implementation of streaming data anomaly detection systems faces challenges, such as achieving high concurrency and smooth scalability to adapt to the continuously increasing data flow. Consequently, the proposed system in this paper incorporates advanced real-time computing techniques, combining principles of high concurrency and seamless scalability to ensure efficient stability while processing large-scale data streams. Simultaneously, the system possesses the capability to support the deployment of machine learning models, enabling users to seamlessly integrate and apply various anomaly detection models. Experimental results demonstrate the exceptional performance of the proposed streaming data anomaly detection system in real-time computing, high concurrency, and seamless scalability, providing a reliable solution for practical application scenarios. This paper contributes robust support for further research and application advancements in the field of streaming data anomaly detection.