Data Analysis and Application of Recorder Based on Neural Network

With the development of science and technology, recorder has gradually become an indispensable tool in various industries. Loggers can record and store a variety of data in real time, from vehicle driving information to industrial equipment status, to medical monitoring data, all reflect its importance. However, these massive amounts of data are just a bunch of meaningless numbers without effective analysis. Therefore, the importance of recorder data analysis is becoming increasingly prominent. However, at present, the application of recorder data has not attracted enough attention, and it is mostly carried out based on traditional methods. Based on this, this paper constructs a two-layer gated recurrent neural network (GRU) model supported by neural network technology for analysis and prediction. Firstly, the optimal model is obtained by comparing the results of GRU models with different parameters. Then, it is compared with the LSTM prediction model with the same parameters. Finally, the reliability of the model is proved by experiments.