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State Grid Xinjiang Enhances AI Knowledge Service Infrastructure

State Grid Xinjiang successfully deploys an advanced AI knowledge service platform, integrating external and internal knowledge resources to enhance work efficiency and accuracy.

Jun 23, 2025Source: Visive.ai
State Grid Xinjiang Enhances AI Knowledge Service Infrastructure

State Grid Xinjiang Successfully Deploys Advanced AI Knowledge Service Platform

URUMQI, China, June 23, 2025 — State Grid Xinjiang Information & Telecommunication Company has successfully completed the deployment and optimization of its knowledge service infrastructure, achieving unified management of cross-professional and internal/external knowledge resources.

Overcoming Challenges in AI Knowledge Services

While the application of artificial intelligence continues to deepen, challenges persist in the knowledge service system and processing modes. These include the limitations of large models, the fragmentation of knowledge within the power industry, and low application efficiency. The new platform aims to establish a comprehensive knowledge hub for the entire business chain, integrating industry knowledge resources and overcoming technical application bottlenecks.

Leveraging RAG Technology for Enhanced Accuracy

By leveraging RAG (Retrieval-Augmented Generation) technology, a mechanism combining “large model context learning” with “high-quality external knowledge input” has been established. Through precise retrieval of knowledge fragments and fusion of multi-source data, the recall accuracy rate has been enhanced to 94.7%, significantly surpassing traditional vector retrieval methods. This ensures the authority and precision of the output content.

Core Capabilities of the Knowledge Service Infrastructure

The knowledge service infrastructure platform possesses three core capabilities:

  1. **Integrated Retrieval of Internal and External Knowledge Bases**: The platform employs a visual configuration system and provides flexible RAG policy support, enabling synchronous retrieval and intelligent matching across multi-source knowledge bases.
  2. **Intelligent Extraction of Multimodal Information**: It supports the extraction and processing of various data types, including text, images, and videos.
  3. **Advanced Question-Answering and Data Analysis Functionalities**: The platform offers sophisticated tools for data analysis and question answering, enhancing decision-making processes.

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, thereby significantly enhancing the accuracy of question answering.

Driving a Shift from Experience-Driven to Knowledge-Driven

This transformation markedly improves work efficiency and quality, driving a profound shift across professional domains from “experience-driven” to “knowledge-driven.”

Future Improvements and Support

Moving forward, State Grid Xinjiang Information & Telecommunication Company will continue to refine the knowledge service infrastructure platform, promoting iterative functional improvements. Persistent efforts will be made in knowledge internal storage management and regular operational support to ensure that the infrastructure services remain both applicable and user-friendly, while maintaining the authority and timeliness of knowledge content. This initiative effectively fosters the deep integration of artificial intelligence technology with power grid construction.

[Related: Future of AI in Healthcare]

Frequently Asked Questions

What is the main goal of State Grid Xinjiang's knowledge service infrastructure?

The main goal is to establish a comprehensive knowledge hub for the entire business chain, integrating industry knowledge resources and overcoming technical application bottlenecks.

How does RAG technology improve knowledge retrieval?

RAG technology enhances recall accuracy by combining large model context learning with high-quality external knowledge input, significantly surpassing traditional vector retrieval methods.

What are the core capabilities of the knowledge service infrastructure platform?

The platform offers integrated retrieval of internal and external knowledge bases, intelligent extraction of multimodal information, and advanced question-answering and data analysis functionalities.

How does the platform enhance work efficiency and quality?

By improving the accuracy of knowledge retrieval and question answering, the platform drives a shift from experience-driven to knowledge-driven operations, significantly enhancing work efficiency and quality.

What future improvements are planned for the knowledge service infrastructure?

State Grid Xinjiang will focus on refining the platform, promoting iterative functional improvements, and ensuring continuous operational support and knowledge content management.

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