An industrial data generation method based on a neural network model

Abstract

The invention provides an industrial data generation method based on a neural network model, and the method comprises the following steps of generating a large-scale data set by taking a time sequencegeneration process as a continuous decision-making process through an identification feedback mechanism based on the idea of a generative adversarial network; wherein the discriminator extracts timesequence characteristics and evaluates the importance of each characteristic to the sequence, and the quality of the generated data is measured by training a real sample and a generated sample, and the discriminator feeds back a corresponding reward value of a data probability generated in each step in the generator through time difference learning, the generator based on the LSTM is trained by astrategy gradient of reinforcement learning, and the reward value is provided by a return value of the discriminator. According to the industrial data generation method based on the neural network model, a new mode of generating big data from small data is realized, so that the mining analysis effect of data mining in a big data environment is improved, and the whole framework has the reliable performance and the excellent expandability.

Description

technical field [0001] The present invention relates to the Internet of Things, industrial data generation, GAN and deep learning, and in particular to an industrial data generation method based on a neural network model. Background technique [0002] At present, small data sets are the eternal pain of big data processing technology. How to analyze the distribution of existing small data, measure the quality of virtual data in the process of data generation, and generate reasonable big data that conforms to the distribution of small data under the constraints of equipment operation rules is a key problem for data mining. [0003] Compared with other data generation methods, the neural network model has the advantages of feature adaptive extraction and generation and processing of high-dimensional and nonlinear data. However, the current research on the use of neural network models for data generation fails to make full use of the ability of neural network model data generat...

AI Technical Summary

Problems solved by technology

However, the current research on the use of neural network models for data generation fails to make full use of the ability of neural network model data generation; the generated data models do not consider the optimal classification relationship between different data categories, resulting in low data generation accuracy

Images