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A Full-Time Cross-Business Interaction Method Based on Multi-Source Data.

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Title: A Full-Time Cross-Business Interaction Method Based on Multi-Source Data.
Authors: Zhou, Digui1 (AUTHOR) diguicsg@126.com, Tan, Qiwen1 (AUTHOR), Huang, Hualin1 (AUTHOR), Tao, Siheng1 (AUTHOR), Qin, Ning1 (AUTHOR)
Source: International Journal of High Speed Electronics & Systems. Aug2026, Vol. 35 Issue 3, p1-21. 21p.
Subjects: Multisensor data fusion, Middleware, Electric power system management, Electric power systems
Abstract: In order to effectively integrate and utilize power multi-source data, avoid information islands, and improve the comprehensive utilization of power data, a full-time and space-time cross-business interaction method based on multi-source data is studied. The intelligent sensing business layer obtains power-related data from various systems, such as geographic information systems (GIS) and distribution management systems (DMS). The full spatiotemporal data fusion business layer extracts spatiotemporal features of power data and matches spatiotemporal correlations between power data and networks. Construct a power spatiotemporal grid coding database, integrate power data from different sources, form a spatiotemporal data system, and store it in the all-spatial and temporal data storage business layer. The application business layer calls the required power data from the all-spatial and temporal data storage business layer to realize the status of power equipment prediction, remote fault diagnosis, intelligent operation and maintenance decision-making, and other functions, among which the power equipment status prediction module combines the differential autoregressive integrated moving average (ARIMA) model and the long short-term memory network (LSTM) model to achieve fusion prediction of power equipment status. Enterprise service bus (ESB), as a communication hub that realizes cross-business interaction of all-time and space-time data in electric power, ensures the smoothness of data interaction between businesses and eliminates the phenomenon of information "islands" between businesses. The experimental results show that this method can effectively fuse power spatio-temporal data, with a data fusion speed of 12 GB/s, capable of processing 513 data fusion tasks simultaneously, with a processing time of only 80 seconds. The CPU utilization rate is 60%, the memory occupation is 180 MB, the accuracy is 92%, the recall rate is 90%, and the F1 score is 91, providing strong support for the intelligent operation and maintenance of power systems. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:In order to effectively integrate and utilize power multi-source data, avoid information islands, and improve the comprehensive utilization of power data, a full-time and space-time cross-business interaction method based on multi-source data is studied. The intelligent sensing business layer obtains power-related data from various systems, such as geographic information systems (GIS) and distribution management systems (DMS). The full spatiotemporal data fusion business layer extracts spatiotemporal features of power data and matches spatiotemporal correlations between power data and networks. Construct a power spatiotemporal grid coding database, integrate power data from different sources, form a spatiotemporal data system, and store it in the all-spatial and temporal data storage business layer. The application business layer calls the required power data from the all-spatial and temporal data storage business layer to realize the status of power equipment prediction, remote fault diagnosis, intelligent operation and maintenance decision-making, and other functions, among which the power equipment status prediction module combines the differential autoregressive integrated moving average (ARIMA) model and the long short-term memory network (LSTM) model to achieve fusion prediction of power equipment status. Enterprise service bus (ESB), as a communication hub that realizes cross-business interaction of all-time and space-time data in electric power, ensures the smoothness of data interaction between businesses and eliminates the phenomenon of information "islands" between businesses. The experimental results show that this method can effectively fuse power spatio-temporal data, with a data fusion speed of 12 GB/s, capable of processing 513 data fusion tasks simultaneously, with a processing time of only 80 seconds. The CPU utilization rate is 60%, the memory occupation is 180 MB, the accuracy is 92%, the recall rate is 90%, and the F1 score is 91, providing strong support for the intelligent operation and maintenance of power systems. [ABSTRACT FROM AUTHOR]
ISSN:01291564
DOI:10.1142/S0129156425405194