Multi-level data fusion and machine learning in intelligent risk control: an automated risk assessment framework
For scenarios such as industrial equipment monitoring and cybersecurity protection where risk events have high complexity and real-time requirements, a multi-level data fusion and machine learning-driven automated risk assessment framework is proposed. The framework integrates sensor time-series data, system logs, and environmental status information, and enhances the risk characterization ability through data-level, feature-level, and decision-level fusion. At the modeling layer, integrated learning and deep networks are jointly used for reasoning to achieve abnormal state recognition and risk scoring output. The system supports online data stream access and low-latency inference deployment, and can complete risk assessment and alarm generation within seconds. The effectiveness of the framework is verified on a multi-source risk dataset, with an accuracy improvement of approximately 8% in risk detection and an inference delay controlled within 50 ms, providing an expandable technical solution for the security risk control of complex systems.