MS-FuLW: A Multiscale Fuzzy Learning Framework With Weak Supervision for Emergency Event Detection

Accurate and timely detection of emergency event holds significant practical value for mitigating potential risks and losses. However, such emergency event often exhibits continuous, gradual evolutionary patterns in the temporal dimension, and this non-stepwise transition introduces inherent ambiguity to the detection task. To address the ambiguity issues in emergency event detection tasks within complex systems, this paper proposes a Multi-Scale Fuzzy Learning method with Weak Supervision (MS-FuLW). The proposed method first constructs a dynamic label mapping mechanism based on fuzzy set theory, extending traditional binary labels into fuzzy labels to characterize the gradual transition process of emergency events. Next, a Multi Scale Convolutional Network (MSC) is designed to extract both local abrupt features and global trend features from time series data in parallel, thereby enhancing the model ability to capture complex patterns, along with a specialized fuzzy loss function to improve compatibility with fuzzy labels. Finally, a weakly super vised learning mechanism is introduced to optimize the fuzzy label modeling process and reduce dependency on precise anno tations. The experimental results show that compared to baseline methods, MS-FuLW steadily improves detection real-time performance by 5%–25% in real environments, providing an effective solution for real-time risk warning in complex systems.