JOURNAL ARTICLE

Research on Engineering Cost Construction System Based on Computer Cloud Computing Algorithm.

  • Published In: International Journal of High Speed Electronics & Systems, 2025, v. 34, n. 3. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Li, Changjiang; Li, Changfang 3 of 3

Abstract

This study is devoted to exploring the method of optimizing engineering cost construction system by using computer cloud computing algorithm. By synthesizing relevant studies at home and abroad, it is found that the current engineering cost construction system generally suffers from slow data processing speed, high computational complexity and insufficient accuracy. In terms of system design, specific technologies, methods and algorithms are adopted. First, a stable cloud computing platform was established to ensure the reliability and stability of the system. Then, techniques such as data mining and machine learning are used to process and analyze a large amount of engineering data to achieve accurate prediction of engineering costs. At the same time, a series of algorithms are designed to optimize the computational efficiency and accuracy of the system. The effectiveness and feasibility of the proposed system are verified through experimental design and analysis. The research results show that the engineering cost construction system based on cloud computing algorithms can significantly improve the efficiency and accuracy of the system, which has high application value and provides a new solution for the engineering construction industry. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of High Speed Electronics & Systems. 2025/09, Vol. 34, Issue 3, p1
  • Document Type:Article
  • Subject Area:Computer Science
  • Publication Date:2025
  • ISSN:0129-1564
  • DOI:10.1142/S0129156425400117
  • Accession Number:185074651
  • Copyright Statement:Copyright of International Journal of High Speed Electronics & Systems is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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