Mechanical Automation Acquisition System Based on Deep Learning and Big Data

In today’s industrial automation field, poor real-time performance, low precision and low efficiency are the common challenges faced in the process of mechanical automation collection. To meet these challenges, AI methods such as deep learning techniques and convolutional neural networks have attracted much attention recently, and have been widely used because of their high efficiency and accuracy in feature extraction and pattern recognition. This paper applied deep learning to this, and convolutional neural networks, as an important branch of deep learning, have attracted attention for their high efficiency in feature extraction and pattern recognition. This article designed a mechanical automation collection system based on convolutional neural networks and big data to improve collection efficiency and accuracy. Firstly, the collected mechanical quantity data was trained using convolutional neural networks. Based on the convolutional neural network model, the collected data was feature extracted. Then, the extracted feature data was classified and clustered to achieve real-time, accurate, and efficient mechanical automation collection. The experimental results showed that the system has significantly improved efficiency and accuracy compared to traditional methods.