The Workload Assessment of National Grid Big Data Projects Based on Content Recommendations and Text Classification


The evaluation of workload in big data project is an important prerequisite for improving the management of the National Grid big data projects. The lack of relevant models and specifications of the evaluation is caused by the exploratory, uncertainty and characteristics of big data projects. Based on the information of typical projects, this paper proposes to combine with Natural Language Processing (NLP)and machine learning to solve the problem. Firstly, this method obtains the reference value of project workload impact factors through content recommendation, neural network and other algorithms. Then the workload estimation model is built based on the measurement of each impact factor. The results show that the method proposed in this paper can effectively identify and predict the attribute class impact factors of the new project, and can also reasonably complete the workload estimation of the project, which is greatly significant to realize the rational allocation of resources.



Software And Hardware

• Hardware: Processor: i3 ,i5 RAM: 4GB Hard disk: 16 GB • Software: operating System : Windws2000/XP/7/8/10 Anaconda,jupyter,spyder,flask Frontend :-python Backend:- MYSQL