Research and Application of Unified Identity Coding Technology for Power Grid Assets
Jie Fu, Ji Wen, KunSan Zhang, YiTing Wang, TaiNing Huang · 2023
Enterprises have accumulated a large amount of data, but due to poor information quality, poor relevance, insufficient Granularity and many other reasons, they cannot form effective support for asset investment decisions. At the same time, there is a lack of real-time information and effective monitoring of the overall condition of assets, and there is no quantitative data analysis of asset management risks, making it difficult to estimate the financial pressure corresponding to maintaining assets and the economic impact on company operations. Therefore, this article has developed a mobile application for engineering material receipt and delivery, a mobile application for engineering on-site inspection and inventory, a mobile intelligent inventory application for fixed assets, and a mobile application for inventory equipment labeling and verification through the actual design of Fujian Electric Power by China State Grid Corporation. At the same time, it has developed a localized scenario application based on the physical ID of the global data center, which connects project coding, WBS coding, material coding, and equipment coding The professional coding in the management of various stages of power grid assets, such as asset coding, enables the interconnection and exchange of information on the status, cost, defects, and other aspects of physical assets in the entire life cycle of planning, design, procurement, construction, operation, maintenance, and retirement. In strict accordance with the relevant standards of the physical “ID” construction of the State Grid Corporation of China, the physical ID construction of main power grid equipment and distribution network equipment is completed in the principle of overall layout, distributed implementation, so as to realize the traceability and sharing of information within the life cycle of power grid assets, and support the analysis and application of enterprise level asset management Big data.