Method For Generating Virtual Atmospheric Environment Data Based On Deep Learning

With the diversification and virtualization of weapon equipment test environments,in order to improve the authenticity and accuracy of virtual tests on weapon equipment,it is necessary to add a virtual atmosphere environment to the virtual test.How to generate virtual atmospheric environment data has become an important research topic.The generation of virtual atmospheric environment data is essentially a time series problem.In recent years,deep learning has performed well in processing large amounts of time series data.This article uses deep learning to generate virtual atmospheric environment data.The specific research done is as follows.First,in order to obtain the training data set and test data set used by the deep learning technology,and to improve the accuracy of the network model,etc.,the atmospheric environment data preprocessing method is studied.The method of dealing with outliers and missing values in atmospheric environment data is studied to ensure that the results generated by the deep learning model will not be biased due to the influence of the two.The standardization method of atmospheric environment data is studied to speed up the network training speed.Finally,the time sliding window processing method of atmospheric environment data is studied,and the training set,verification set and test set required by the deep learning model are obtained by processing the Jena data set in Germany.Secondly,in order to quickly and accurately generate atmospheric environmental data,based on the characteristics of multiple parameters in the atmospheric environment with physical correlation,unlike most traditional singleparameter methods,this paper proposes a merged long-short-term memory network(M-LSTM)model.Use multiple atmospheric parameters with strong correlation to generate specific atmospheric parameters to improve accuracy.Taking temperature as an example in this paper,the relationship between other parameters in the atmospheric environment and temperature is analyzed using data correlation analysis methods,and multiple LSTM networks that generate temperatures based on different parameters are combined to make full use of the features contained in the original data.The final experimental results show that the mean square error of the test set of the M-LSTM-AH model is at least 9.3% lower than that of the classic LSTM-AH model and accurate temperature data generation is achieved.Finally,for the problem that the multi-parameter atmospheric environment data is needed in the joint test environment,this paper proposes the LSTM-GRU-multi network model.Because the atmospheric environment data samples have many characteristics and huge data volume,it takes a lot of time to train using the LSTM model alone.Compared with LSTM,GRU has a simpler structure,and the network parameters are reduced to converge faster.This article combines LSTM and GRU to speed up the network training speed under the premise that LSTM effectively processes a large amount of atmospheric environmental data.At the same time,because the single-layer network structure is too simple,it is far from meeting the needs of processing multiple characteristics of atmospheric environmental data.This paper uses a multi-layer network structure.Multiple sets of experiments on the Jena data set show that the LSTM-GRU-multi model reduces the MSE on the test set by at least 11.65% compared with the traditional multi-layer LSTM model,and also has a 2.3% advantage in training time.LSTM-GRU-multi model can accurately and quickly generate multi-parameter atmospheric environment data.