Abstract
Aiming at the project demand forecasting problem based on multisource data fusion, a multisource heterogeneous data fusion model is established, and the unified quantitative representation method of heterogeneous data based on triangular fuzzy numbers is studied, and the ordered weighted average operator is used to integrate the preferences of decision-makers. A multisource heterogeneous data fusion algorithm that supports multiuser decision-making is designed. Based on the analysis of the internal and external environment of human resources in a company’s engineering positions, this paper qualitatively analyzes and selects the factors affecting the demand for talents in key positions in a company based on the characteristics of demand influencing factors and finds out the quantifiable and influential factors from the representative factors of talent demand for key positions in a company. Using historical data, statistical methods are used to process the eight related factors of a certain company, which confirms the factors that have a greater impact on the demand for talents in key positions of a company and influences the demand for talents in key positions in companies of the same type. The identification of factors provides a basic argument for a company. According to the results of statistical analysis and the characteristics of existing data, two variables of factory output and time are selected to be used in regression analysis forecasting model and gray system forecasting model of a certain company to predict the demand for key talents of a certain company. The company finally adopts combined forecasting. The method determines the predicted value of the talent demand for a certain company’s key positions. According to the results of demand forecasting and the current status of human resource management in a company, this article proposes a company’s key position talent management planning measures, in order to provide a reference for the management of a company’s key company position talents and ensure a company’s key company positions in the future talent demand reserve.
Highlights
In the process of enterprise informatization construction, due to the phased, technical, and other economic and human factors of the construction of various business systems and the implementation of data management systems, enterprises have accumulated a large number of business data in different storage methods during the development process
Human resource forecasting can be divided into human resource demand forecasting and human resource supply forecasting, including the dual meanings of foreseeing and measuring the future
Enterprise human resource demand forecasting is to predict human resources, which is a complex system, so, it must be completed based on a scientific forecasting model. is article has already sorted out the existing human resource demand forecasting models
Summary
In the process of enterprise informatization construction, due to the phased, technical, and other economic and human factors of the construction of various business systems and the implementation of data management systems, enterprises have accumulated a large number of business data in different storage methods during the development process. Each node conceptually includes functions, such as data association, correlation, and evaluation On this basis, Boss 6 colors further developed and proposed a set of modeling and simulation methods to realize the design of data fusion system, as can be seen, which mainly explains the whole system of data fusion system and its content. In the face of the emerging scientific theory and method of data fusion, it is necessary to conduct in-depth systematic research on the existing data fusion technology and find its fit with the field of natural language processing from both theoretical and practical levels. E. combination of sensor network and data fusion technology proposes a Kalman filter batch estimation fusion algorithm; some literature has studied the fusion method of massive multisource heterogeneous data in the Internet of ings environment and has been successfully applied in the process of target positioning and tracking. There are few applied researches on human resource forecasting methods in specific enterprises
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