This paper proposes a deep learning-based multimodal signal fusion and optimization algorithm model to solve the problem of intelligent analysis and emergency response of oil and gas pipeline safety events. The wavelet transform is used to extract the time-frequency domain features of the vibration signal, and the artificial bee colony algorithm is designed to optimize the classification parameters of the support vector machine. The SVD algorithm is selected to reduce the dimensionality to reduce the redundant features and optimize the computational efficiency. In the design of emergency response strategy, a closed-loop management mechanism including leakage detection, graded response and effect evaluation is constructed. The experimental results show that the F1 value of the SVMABC-WT-SVD model reaches 0.994 and mAP@0.5 reaches 99.8% in the ablation test, which is 3.6% and 2.9% higher than that of the SVM model, respectively. On-site stress test verified that the average response latency of the system in high concurrency scenarios is less than 3ms, which meets the real-time emergency response requirements.
