Model establishment and risk management model of industrial big data based on neural network

Abstract

With the maturity of big data, the data hierarchy and data processing methods of big data are changing with each passing day. Big data has begun to occupy an important position in all walks of life, and data is an asset. Industrial risk has always been the focus of data generation and application. In this paper, we use TensorFlow to implement a big data classification model, and add different optimization methods and convolutional neural networks. In the research, the usage scenarios of different optimization methods and the characteristics of self-sufficiency were compared, so that the final classification accuracy of the model reached 99.67%. Using TensorFlow to build different types of neural networks to fit the Fourier transform, and use the trained model for ultrasonic detection of time-domain signals, and obtain better signal processing results. On this basis and analyze the characteristics of different models, as well as the advantages and disadvantages of signal processing.