With the vigorous development of China's market economy and the continuous growth of people's livelihood demand, industries such as transportation, construction, and electricity are facing dual challenges of tight funding and surging construction demand. In this context, the rapid development of information technology, especially the rise of big data and cloud computing, has brought unprecedented opportunities for engineering cost control. However, the application of cost big data is still in its early stages, limited by data processing and analysis capabilities. This study is therefore committed to exploring new methods of engineering cost control in the big data environment. This study builds an intelligent, information-based, efficient and accurate control system, defines the control connotation and requirements, and designs the system framework and principles. This study proposes an innovative control method based on big data mining, and constructs a project cost case database and prediction model. At the same time, artificial intelligence algorithms such as neural networks and support vector machines are introduced to improve prediction and control accuracy. The technical roadmap of this study comprehensively covers the entire process from theoretical construction to method innovation, to model design and case verification, aiming to provide timely and accurate control strategies for engineering construction, optimize resource allocation, reduce capital waste, and provide scientific and practical solutions for engineering cost control in the big data environment.

Yiru Zhang. Research on Optimization of Engineering Cost Database Based on Big Data and Intelligent Technology. International Journal of New Developments in Engineering and Society (2024) Vol.8, Issue 5: 42-47. https://doi.org/10.25236/IJNDES.2024.080507.

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