A Dynamic Perception Algorithm for Security in Scale Intelligent Job Scenarios Based on Machine Learning

The security in large-scale smart job scenarios requires timely response and processing. Therefore, a machine learning based dynamic perception algorithm for security in large-scale smart job scenarios is proposed. Using wavelet transform method to denoise large-scale intelligent homework scene data. Using convolutional neural networks, construct a feature extraction model for large-scale intelligent job scene data, and extract the denoised features of large-scale intelligent job scene data. On this basis, based on the K-means algorithm in machine learning, dynamic perception of security in large-scale intelligent job scenarios is achieved. The experimental results show that the proposed algorithm has a high accuracy in dynamic perception of security in large-scale smart work scenarios, and can effectively shorten the time for dynamic perception of security in large-scale smart work scenarios.