Research on intelligent high air anti-fall device based on neural network

With the improvement of safety requirements of high altitude operation, the research and development of efficient anti-fall devices has become an important task in the industrial field. In this paper, an intelligent high-altitude antifall device based on neural network is proposed to improve the safety and efficiency of high-altitude operation. First, we use convolutional neural networks (CNN) and Secret sharing (SS) techniques to train and model operator behavior patterns. By analyzing and learning from key patterns and trends in the data, our systems are able to monitor the operator's status in real time, predict potential fall risks, and take timely protective measures. In addition, we introduce a new neural network model, the Secret Neural Model (CNS), by combining the above two techniques and improving them. The new model not only enhances the adaptability and response speed of the anti-fall device in the complex environment, but also greatly reduces the fall accident and cost. Through a series of experiments and performance evaluation, we prove the effectiveness of the intelligent high air fall prevention device in improving the safety of working at altitude. The experimental results show that the neural network-based system can not only effectively prevent fall accidents, but also reduce false positives through intelligent adjustment, thus reducing the overall operating cost. This research provides new technical support for the safety of working at altitude, and has important practical application value for promoting industrial safety production and preventing the risk of working at altitude.