Abstract: To address the critical challenges in power standard management including poor real-time information, weak knowledge correlation, and prominent service passivity, a dynamic knowledge service system based on the collaborative drive of distributed monitoring, natural language processing, and knowledge graph is proposed. The system relies on a distributed monitoring engine to achieve second-level data collection from multiple source heterogeneous sources, reducing the standard update detection delay from 120 hours (with manual inspection) to 5.8 hours. Through dynamic knowledge graph construction technology driven by deep learning, the system breaks through the semantic limitations of traditional keyword search, increasing the knowledge association recall rate to 96.2%. It also pioneers a proactive early warning mechanism empowered by graph reasoning, promoting the service model to shift from passive response to on-demand push. After a high-concurrency pressure test of 1 752 requests per second, the system's 95% request response time remains stably below 300 ms, achieving coordinated optimization in terms of timeliness, completeness, and robustness. This system provides a reusable technical path for the power industry to build an enterprise-level "digital standard library".

Key words: dynamic knowledge service, power standards, distributed monitoring, knowledge graph, real-time updating