Analysis of high-level dance movements under deep learning and internet of things

The purpose is to improve the design effect of high-level dance movements and help dancers to better master these movements. The body changes with advanced dance movements based on deep learning (DL) algorithm and Internet of Things (IoT) technology are analyzed to promote the practical application of biological image visualization technology. Firstly, DL is applied for image recognition, and secondly, the technical architecture of IoT technology is constructed. Finally, DL and IoT-edge computing (IoT-EC) are employed to establish the network structure of the dance generation model. The experimental results indicate that IoT-EC based on DL significantly enhances the efficiency of resource allocation and effectively reduces the server processing time. For 200 tasks in the workspace, deep reinforcement learning can be optimized in only 8 s. When there are 800 tasks in some workspaces, the edge server takes 21 s to optimize deep reinforcement learning (DRL). Besides, this scheme can control the energy consumption of the server in the calculation process while dramatically reducing the average waiting time. The application of these technologies in the dance movement has extensively promoted the progress and development of the dance industry. The present work provides references DL in image recognition and remote sensing image classification.