RETRACTED: Evaluation Model of Human Resource Allocation Balance for Healthy Environment in Organizations Based on Big Data Driven and Internet of Things

ABSTRACT Following an investigation undertaken by the publisher, we have determined that this paper was accepted on the basis of a compromised peer review process. We hereby retract the paper. The corresponding author has been notified of the retraction. The retraction statement can be found here: https://doi.org/10.1520/JTE20269995. To improve the ability of balanced evaluation, dynamic allocation, level of output, and profitability of human resources, a balanced evaluation model of human resources allocation is proposed based on big data driven and the Internet of Things. An evaluation architecture model of human resource allocation balance in view of big data driven and the Internet of Things is established; a multithread big data driven configuration model and the Internet of Things are used to construct a table model of enterprise human resource optimal allocation in the form of reports; statistical regression analysis method is used to detect the risks and process parameters in the process of enterprise human resource optimal allocation; and employment elasticity theory analysis is taken to establish a resource factor analysis model of human resource balanced allocation under the constraint of economic growth mode. According to the changing trend of the scale and structure of labor resources, statistical regression analysis is adopted to make big data driven analysis in the process of balanced evaluation of human resources allocation, and balanced game control is adopted to analyze the relevant factors affecting human resources allocation to realize balanced scheduling of population flow and human resources allocation and automatic post adjustment. The empirical analysis results show that the system has a good balance in human resource allocation, the output efficiency among the various elements of human resource allocation is improved, and there is a high level of efficiency in human resource output.