This research is dedicated to exploring the application of large language models
in the Beijing Subway scientific research project management platform. It
conducts a thorough analysis of many key elements, including the application
background, technical support, practical achievements, and future development
paths.
With the continuous development of the Beijing Subway construction scale, the
number and complexity of scientific research projects have been gradually
increasing. Traditional management models are getting more and more insufficient
in dealing large amounts of data, complicated processes, and precise
decision-making requirements. By using natural language processing, machine
learning, knowledge graph pedigreestechnological and technical model related
technologies, which are very different from the one of the most inventive ones,
are presented. The objective of intelligence is to solve this model by
automatically analyzing papers with a logical and scientific approach and
logically forecast project development and cost. This not only greatly increases
managerial efficiency; it also puts more and more rationalization into the
industry, which is why the intelligent development of the business is encouraged
to some extent and makes decision-making in a way that is both more
sensible.
But there are still some problems that the use of these kinds of models has to be
done. Issues concerning the quality of data; these included data that was either
inaccurate; these had an effect on the models’ repeating. Numerous research
organizations are also severely financially dependent because of the volume of
data that is used; these problems are made worse by technical difficulties.
Furthermore, because of the variations between the various types of data and the
incompatible interfaces, the system integration operation is very
complicated.
These barriers are predicted to be resolved by huge language models in the
future. They might be more intelligent and include many kinds of data in their
integration. The rail transportation industry is going to see a greater use of
this development in research and development, which will give the rail transit
business a more complete view and more knowledgeable decision-making.