Research on Search Intent Prediction for Big Data of National Grid System Standards

ABSTRACT

Smart grids are becoming more complex due to the development of big data., and technical documents and institutional standards are constantly updated. As a result, It is difficult for workers in different positions to obtain the required information and data. This thesis is oriented towards this problem, and combined with deep learning algorithms to build a user intent prediction model based on the existing knowledge map. By extracting user characteristics and using a dynamic matching algorithm, the purpose of intent prediction is achieved. In this way, the required standards and requirements can be found faster and more directly in the work process, which effectively improves the working efficiency of employees and reduces the difficulty of learning and training.

1. INTRODUCTION

With the rapid development of information technology, the field of artificial intelligence has gradually penetrated into all walks of life. Especially in the core technology field, which has a far-reaching impact. Today, with the continuous improvement and development of State Grid Science and Technology, facing the iterative and updated technical equipment. As well as the increasing number of new standards and new requirements, the database has also been continuously expanded and accumulated. However, for employees in different positions, the technical standards are also different. Most of today's user intent predictions are mainly improved on the basis of search engines, and at the same time, the database structure has been improved.