State Grid Xinjiang Boosts Power Grid with AI-Powered Knowledge Service Infrastructure

URUMQI,China – State Grid Xinjiang Information & Telecommunication Company has successfully deployed and optimized its knowledge service infrastructure,marking a significant advancement in power grid technology. The project, completed on June 20th, 2025, enables unified management of internal and external knowledge resources across various professional domains.

AI and RAG Technology Drive Efficiency

The deployment addresses challenges in the power industry related to the limitations of large AI models, knowledge fragmentation, and low application efficiency. The new platform serves as a comprehensive knowledge hub, integrating industry knowledge resources to overcome technical obstacles. By leveraging retrieval-Augmented Generation (RAG) technology,the system combines “large model context learning” with “high-quality external knowledge input,” achieving a recall accuracy rate of 94.7%, surpassing customary vector retrieval methods [1].

Did You Know? RAG systems improve question answering accuracy by retrieving relevant information from a knowledge base before generating a response.

Core Capabilities of the Platform

The knowledge service infrastructure platform offers three core capabilities:

• Integrated retrieval of internal and external knowledge bases
• Intelligent extraction of multimodal information
• Advanced question-answering and data analysis functionalities

The platform employs a visual configuration system and provides flexible RAG policy support, enabling synchronous retrieval and intelligent matching across multi-source knowledge bases.

Enhanced accuracy and Efficiency

To date,the company has gathered 19 requirements for RAG knowledge base construction and over 19,000 knowledge documents. Among these, 710 documents related to the intelligent judgment scenario of power economic relations have undergone knowledge slicing processing and knowledge space construction testing.The scenario application has been successfully integrated via API interfaces, with the recall accuracy of slice data increasing by more than 40% compared to the original method. This transformation drives a shift from “experience-driven” to “knowledge-driven” practices.

Pro Tip: Knowledge slicing involves breaking down large documents into smaller, more manageable chunks for efficient retrieval and processing .

Future Developments

State Grid Xinjiang plans to continue refining the knowledge service infrastructure platform, promoting iterative functional improvements.Efforts will focus on knowledge internal storage management and regular operational support to ensure the infrastructure services remain applicable, user-friendly, and maintain the authority and timeliness of knowledge content. This initiative fosters the deep integration of artificial intelligence technology with power grid construction.

The Rise of Retrieval-Augmented Generation (RAG)

RAG is emerging as a pivotal technology in the realm of AI-driven knowledge management. By combining the strengths of large language models with external knowledge sources, RAG systems enhance the accuracy and relevance of information retrieval. this approach is particularly valuable in industries like power grid management, where precise and reliable information is critical for operational efficiency and decision-making .

The success of State Grid Xinjiang’s implementation underscores the potential of RAG to transform knowledge management practices across various sectors. As AI continues to evolve, RAG is poised to play an increasingly important role in ensuring that AI systems have access to the most accurate and up-to-date information.

What other innovative technologies could further enhance power grid efficiency? Share your thoughts in the comments below!

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