Abstract
Today's society has entered a new era of scientific and technological progress and
rapid economic and social development. International information and academic
exchanges have also been strengthened. The impact of computer technology on economic
and social development and the improvement of people's lives has also become
increasingly prominent, and the way of human survival and thinking has also changed.
The traditional self-driving travel strategy query adopts manual management method, but
this management method has many disadvantages, such as low efficiency, low security
and inaccurate information transmission. At the same time, because the self-driving
travel strategy query will form a large number of personal documents and information
system data, the centralized management of tourist attractions, travel notes, travel routes,
etc. through manual methods will form a search Change and maintenance and other
troublesome management problems. At the same time, as the demand of users for
network technology is also increasing, information technology also needs to continue to
carry out new reforms to meet the needs of the times. According to this problem, the
research and development of a self-driving travel strategy query system can not only
greatly improve the efficiency of information retrieval, change and maintenance, but also
facilitate the management and application of information systems, thus reducing
information management costs and improving efficiency.
The self-driving travel strategy query system is designed with B/S architecture, and
developed with java language and spring boot framework. The system mainly designs
and completes the functions of user registration and login, personal information
modification, user, scenic spot type, tourist attractions, travel notes, classified labels,
tourist routes, etc. in the management process. The system is easy to operate and the
interface design is simple. It can not only basically meet the daily management work of
the industry, but also effectively reduce personnel costs and time costs, providing
convenience for the inquiry and management of self-driving travel strategies.
