Privacy-preserving decision-making method for electrolytic aluminum production
In the process of electrolytic aluminum production,the traditional manual control decision-making method has been dif-ficult to adapt to the requirements of modern aluminum electrolysis production.Deep learning algorithms have been widely used to process such time series data,and the efficiency of decision-making affects the stable operation of the aluminum electrolytic cell and the efficient output of aluminum.At the same time,data privacy issues can not be ignored.Privacy security not only affects the production process of electrolytic aluminum,but also affects its normal use.Misuse and abuse of data mining may lead to the leakage of user data,especially sensitive information.Once the information is lost or leaked,it will cause significant losses.In order to solve the above problems,this paper proposes an electrolytic aluminum decision-making method based on the improved LSTM model structure combined with the optimized ElGamal algorithm.Firstly,the optimized ElGamal algorithm is proposed to solve the problem of data privacy.Then according to the characteristics of electrolytic aluminum data,the LSTM model structure is improved and the ElGamal algorithm is optimized.Experimental results show that the performance of this method is better than that of traditional methods under the condition of ensuring the privacy and security of decision-making.It has reference value in actual situations.
万方数据
维普
