Research on the prediction algorithm about the number of workers returning to work based on the COVID-19
Bingtao Gao, Zhengang Zhai, Lei Wang, Zhiyuan Pan, Song Xu, Dan Liu, Yijia Hu, Junyan Wang · 2020
In order to make rational use of the resources about epidemic prevention such as masks, and prevent the leaders of enterprises from falsely reporting the number of workers back to work, the evaluation of the number of workers back to work in enterprises is transformed into the prediction of the number of workers back to work under different distribution about the number of workers back to work. Based on the analysis of the existing historical data, this paper predicts the number of people who return to work through intelligent algorithm, so that the government can prepare and distribute epidemic prevention materials. Based on the analysis of the daily power consumption data of the enterprise, combined with the existing number of enterprises returning to work, this paper constructs a prediction model of the number of enterprises returning to work based on the power consumption, and completes the prediction of the number of enterprises returning to work in the future. The experimental results based on the simulation data of the number of enterprises returning to work with different distributions show that the intelligent algorithm can effectively predict the number of enterprises returning to work under the background of COVID-19.