Research on the Statistical Analysis Technology of Power Grid Engineering Project Category Cost Based on Big Data Analysis Technology
- DOI
- 10.2991/978-94-6463-742-7_3How to use a DOI?
- Keywords
- big data analysis technology; power grid engineering; project category cost; statistical analysis technology
- Abstract
For power grid engineering projects, the project consists of a number of projects to clear a certain amount of engineering, the completion of the project involves a series of different categories of costs, the existing technology of these categories are usually defined as labor costs, mechanical equipment costs, material costs, management fees, profits and taxes. Therefore, how to efficiently analyze these huge category of data based on data analysis technology has become an urgent technical problem to be solved. Therefore, in order to solve the defects in the existing technology that it is difficult to compare and analyze the cost data of many power grid categories, this paper puts forward the statistical analysis technology of the cost of power grid project categories based on big data analysis technology.
- Copyright
- © 2025 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Hongyi Chen AU - Huiying Wu AU - Minquan Ye AU - Biao Chen PY - 2025 DA - 2025/05/31 TI - Research on the Statistical Analysis Technology of Power Grid Engineering Project Category Cost Based on Big Data Analysis Technology BT - Proceedings of the 2025 4th International Conference on Bigdata Blockchain and Economy Management (ICBBEM 2025) PB - Atlantis Press SP - 14 EP - 24 SN - 1951-6851 UR - https://doi.org/10.2991/978-94-6463-742-7_3 DO - 10.2991/978-94-6463-742-7_3 ID - Chen2025 ER -