Real-Time Monitoring and Image Recognition System for Abnormal Activities in Financial Markets Based on Deep Learning

As the complexity and dynamic changes in financial markets continue to increase, real-time monitoring of abnormal activities has become a critical task in financial regulation and risk management.Traditional monitoring methods, which rely on rules and experience, struggle to handle the nonlinear and highly volatile nature of financial markets, especially when dealing with large-scale and multidimensional data.In recent years, the rapid development of deep learning technology has provided new solutions for real-time monitoring of abnormal activities in financial markets.By transforming time-series data into images and leveraging the pattern recognition capabilities of deep learning, abnormal market fluctuations can be more accurately detected, enabling efficient early-warning systems.However, existing research still faces challenges such as inadequate data adaptability, difficulties in integrating multidimensional information, lack of real-time performance, and poor interpretability of warning systems.This paper proposes a deep learning-based realtime monitoring and early-warning system for abnormal activities in financial markets, which consists of two main components: first, a real-time monitoring model for financial market time-series curve patterns based on information block recognition, aimed at extracting key features from time-series data for precise market fluctuation monitoring; second, an early-warning method for abnormal activities based on the changes in time-series curve trends, designed to identify potential abnormal activities in real time and issue earlywarning signals.The core value of this study lies in the proposed innovative monitoring model and warning mechanism, which overcome the limitations of traditional methods and provide a more accurate and real-time tool for abnormal activity monitoring and early warning, with significant theoretical and practical value.