The Design And Implementation Of Intelligent Management System For Operational State-owned Assets

The widespread application of information technology in various management systems has greatly improved the level of asset management in enterprises.However,with the increasing competition in the market,simple informationization of asset management can no longer meet the requirements of business operations.As the country’s attention to the loss of state-owned assets due to imperfect management mechanisms continues to increase,it is particularly important to establish an intelligent management system for operating state-owned assets that can adapt to market development needs and meet the management needs of enterprises and institutions using information technology and artificial intelligence.The thesis conducts research on the intelligent application of operating state-owned assets management system,builds a business scenario and management approach based on big data analysis,studies and discovers rules through data mining algorithms for asset accounts collected and summarized by the asset management module,financial module,and leasing system.It also analyzes the source and flow of income through big data analysis.Based on big data analysis,it studies precision management of operating state-owned assets to improve the efficiency of asset use.This thesis explores the application of big data technology in the entire lifecycle of operating state-owned assets management.It also studies the classification of operating state-owned assets based on BP neural network,SVM,and CART algorithm for asset revenue prediction and risk warning,reducing the risks faced by operating state-owned assets and avoiding asset loss.The thesis analyzes the intelligent application and key technologies of operating state-owned assets intelligent management system,designs an operating state-owned assets intelligent management system including user center,basic configuration,asset management,contract management,revenue management,and data reporting modules.The system adopts microservice architecture and is developed using a three-layer framework structure.The main framework uses technologies such as Spring Boot,Spring Framework,Apache Shiro,etc.,which can analyze data analysis information and predict results based on big data.Based on the classification and prediction models based on big data and artificial intelligence,this thesis designs and implements corresponding intelligent analysis and management modules,completing the system deployment and testing.Currently,this intelligent asset management system has been put into use,achieving full coverage of asset operation management and supervision,effectively improving the level of operating state-owned assets management.