Practice of intelligent algorithm in engineering fund statistics and budget optimization

Abstract

With the continuous expansion of the scale of engineering construction industry and the increasing complexity of projects, the
traditional model of engineering fund statistics and budget management has been difficult to meet the needs of precision, dynamics and efficiency. Focusing on the practical application of intelligent algorithms in this field, this paper first expounds the importance and challenges of
engineering fund statistics and budget optimization, and then systematically combs the core principles and adaptation scenarios of intelligent
algorithms such as machine learning, deep learning and big data analysis. Through specific case analysis, the implementation path and application effect of intelligent algorithm in key links such as automatic collection and cleaning of engineering fund data, cost prediction and
risk assessment, dynamic adjustment of budget and optimal allocation of resources are discussed in detail. The research results show that
the introduction of intelligent algorithm can significantly improve the accuracy and efficiency of fund statistics, enhance the scientificity and
foresight of budget forecast, effectively reduce the risk of project cost overrun, and provide strong data support for project management decision-making. Finally, this paper summarizes the technical bottlenecks and application problems in current practice, and looks forward to the
development trend of deep integration of intelligent algorithm and project fund management in the future, aiming at providing reference for
theoretical research and practical application in related fields.