Weak Signal Detection Technology in Big Data Based on Knowledge Base

With the development of science and technology and the progress of data processing technology, big data weak signal detection is playing an increasingly important role in scientific research and engineering application. However, the existing detection systems face three main challenges: high false alarm rate and redundant information problems, difficulty in identifying multi-target dispersed homologous signals, and complex signal detection problems with spatio-temporal dispersion. To solve these problems, this paper proposes a weak signal detection model based on knowledge base, by integrating the data of distributed computing framework and ESOM time series analysis methods into the system design, and adds data filtering and signal analysis and state evaluation engine to the existing detection sensors. The experimental results show that this method not only significantly reduces the systematic false alarm rate, but also effectively improves the detection ability of complex signals, providing a new technical path for weak signal detection in big data environment.