Intelligent Metadata Governance for Power Data: Background, Solution, Value and Case Studies

Background

To modernize national governance and unlock the value of data assets, Southern Power Grid Yunnan has managed over 500 TB of power‑related data supporting hundreds of applications across energy, government and public services. Traditional data‑governance methods struggle with multi‑source heterogeneous data, low automation, and outdated standards.

Challenges

1) Manual collection of management and business metadata; 2) Inconsistent data standards across business lines; 3) Limited coverage of quality‑rule catalogs; 4) Tight coupling of data logic and business processes; 5) Fragmented governance knowledge.

Goals

The governance initiative aims to achieve five objectives: intelligent multi‑source metadata acquisition, knowledge‑graph construction, automatic generation of data standards and quality rules, dynamic rule updates, and intelligent problem analysis.

Solution Architecture

The solution consists of two layers: (1) a foundational data‑governance service layer built on machine‑learning, natural‑language‑processing and intelligent algorithms; (2) an intelligent data‑governance framework that leverages the service layer to provide a unified platform.

Key Technical Steps

1) Construct a unified metadata model to support intelligent recognition of heterogeneous sources. 2) Build a power‑domain knowledge graph linking technical, business and management metadata. 3) Automatically generate data standards and quality‑rule scripts from the knowledge graph. 4) Use feature‑analysis algorithms to discover and update data standards. 5) Apply machine‑learning to automate quality‑issue attribution and recommend remediation based on historical cases.

Core Technologies

1) Unstructured‑data metadata perception (NLP, image and audio recognition). 2) Power‑metadata knowledge‑graph construction. 3) Data‑standard mining via feature‑analysis algorithms. 4) Adaptive quality‑rule generation engine producing executable SQL scripts. 5) Automated quality‑issue root‑cause analysis using similarity‑based matching.

Typical Applications

• Cloud‑Power‑Loan: leveraging power‑usage data to build credit profiles for small‑business loans, increasing data‑service success rate from 81.3 % to 95.7 % and supporting over 2 000 accesses. • Caiyun‑Charge: reducing data‑issue rate from 8 % to 2 % for electric‑vehicle charging data, enabling real‑time statistics and precise charging‑pile placement.

Value Delivered

After one year, the intelligent governance platform has managed more than 5 000 data assets, auto‑generated over 3 000 quality rules, resolved 1 000+ issue types, and supported 20+ application scenarios, effectively unlocking the value of power data across banking, renewable energy and customer services.