Overview
We delivered a multi-agent system that mapped PI points to physical equipment across SCADA, maintenance, and operational databases. This resolved inconsistent tag names and context gaps, resulting in a live knowledge graph that enhanced diagnostics, visibility, and automation.
Challenge
- Inconsistent Tag Naming: Equipment appears under different aliases in different systems, making entity resolution difficult.
- Manual Mapping Effort: Current mapping processes rely on tribal knowledge and manual workflows.
- Low Interoperability: Fragmented systems lack semantic alignment, limiting analytics and automation.
Solution
- Agent-to-Agent (A2A) Reasoning: Agents with self-reflection capabilities collaboratively resolve tag identities and context.
- NLP + Ontology Alignment: Extract and map tag descriptions using symbolic reasoning and domain ontologies.
- Continuous Refinement: Feedback loops enable live updates to mappings, improving accuracy over time.
Key Benefits
- Unified Equipment Intelligence: Seamless visibility across SCADA, maintenance, and operational databases.
- Automated Knowledge Graphs: Rich, evolving maps of equipment-tag relationships.
- Scalable Deployment: Adaptable to large, multi-system industrial environments.
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