Research on the Operational State of Information and Communication Systems Based on Multi-Source Data Aggregation

Addressing the existing challenges of decentralized management and the lack of a unified platform for comprehensive control over the operation status of power grid equipment, maintenance operations, and resource integration, this study proposes a design for an intelligent power grid maintenance and inspection information platform based on multi-data fusion. The holistic architecture of the intelligent power grid maintenance and inspection information platform is formulated, encompassing components such as the maintenance and production management system, real-time control system, and intelligent power grid maintenance and inspection management platform.To effectively integrate data from diverse sources, a forward neural network model utilizing Hermite orthogonal polynomials as stimuli is employed for the fusion of multi-source data within the power grid, enabling accurate analysis of the grid's operational state. Experimental testing is conducted using the developed intelligent power grid maintenance and inspection information platform. The results of the experiments indicate that the platform can monitor equipment status in real-time and provide functionalities such as fault prediction, effectively enhancing control over on-site equipment and maintenance operations.