The Design And Implementation Of Intelligent Talent Management System For State-owned Enterprises

Nowadays,due to the flawed analysis and evaluation functions of their existing human resource system and limited application of results,many SOEs(State-owned Enterprises)do not have an information system to monitor and analyze the data for talent management.In terms of talent selection and utilization,some units and departments are not fully aware of the need to respect the objective laws of growth of talent cadres,focusing excessively on"seniority","education" or "age".There is no scientific basis for the promotion and appointment of cadres.Also,because of the neglect of sustainable echelon building,their leadership teams have the defects of overall old age,unbalanced age distribution and low percentage of young leaders,suggesting a concentrated exit of leaders in the coming years.In view of this,this thesis designs and implements an intelligent talent management system for SOEs.By comprehensively analyzing the data distribution and personal holograms of enterprise cadres in all dimensions,combined with machine learning,it realizes the prediction of cadre promotion and appointment to support the scientific cultivation and construction of SOEs’ cadre teams of different ages.Based on analysis of the background of system construction,the paper analyzes the specific requirements,designs the system architecture,database tables and introduces system implementation in detail.In addition,it also describes the process of implementing the functional modules of the intelligent talent management system.The system as a whole uses the B/S architecture.In terms of business application,it adopts the front-and back-end separation mode,with the front-end using Vue framework to realize component-based construction,and the back-end using SpringBoot+MyBatis to realize rapid development of business logic.In terms of database,it employs MySQL for persistent data storage and Redis for cache processing.In terms of cadre promotion and appointment prediction,it exploits the lightGBM algorithm,which is trained by historical cadre promotion and appointment data.The promotion and appointment prediction model is able to predict the promotion and appointment of cadres on a regular basis,providing a scientific basis for the cadre reserve and talent selection of managers.Besides,the system is clustered overall,with the front-end deployed to Nginx and the back-end to Tomcat.The prediction model is deployed to a separate server,where data processing and prediction are performed regularly through scheduled tasks,and the prediction results are synchronized to MySQL business data tables.Through the front-and back-end separated development and deployment,the system has considerable scalability and maintainability to fully meet the goals of rapid expansion and continuous upgrade.In general,the system consists of overall analysis,team analysis,cadre portrait,relationship identification,echelon construction,cadre adjustment,query statistics and system management.Through multi-dimensional analysis from individual cadres to leadership teams,it provides data support for managers in talent training and team building.At present,the system has been launched and applied in many SOEs,with its functional modules well meeting clients’ need.Whether it is a comprehensive portrait of individual cadres or the composition and trend analysis of each dimension of the team,it provides clear and effective data for managers’ talent selection and utilization and team building.As real applications demonstated the intelligent talent management system plays a crucial role in the decision-making of cadre training and selection.