This diagram illustrates a sophisticated Intelligent Event Processing architecture that utilizes Retrieval-Augmented Generation (RAG) to transform raw system logs into actionable technical solutions.
Data Stream (Event Log): The system captures real-time logs and events.
Importance Level Decision: Instead of processing every minor log, this “gatekeeper” identifies critical events, ensuring the AI engine focuses on high-priority issues.
2. The RAG Core (The Reasoning Engine)
This is the heart of the system (the pink area), where the AI analyzes the problem:
Search (Retrieval): The system performs a Semantic Search and Top-K Retrieval to find the most relevant technical information from the Vector DB.
Augmentation: It injects this retrieved context into the LLM (Large Language Model) via In-Context Learning, giving the model “temporary memory” of your specific systems.
CoT Works (Chain of Thought): This is the “thinking” phase. It uses a Reasoning Path to analyze the data step-by-step and performs Conflict Resolution to ensure the final answer is logically sound.
3. Knowledge Management Pipeline
The bottom section shows how the system “learns”:
Knowledge Documents: Technical manuals, past incident reports, and guidelines are collected.
Standardization & Chunking: Data is broken down into manageable “chunks” and tagged with metadata.
Vector DB: These chunks are converted into mathematical vectors (embeddings) and stored, allowing the engine to search for “meaning” rather than just keywords.
4. Final Output
RCA & Recovery Guide: The ultimate goal. The system doesn’t just say there’s an error; it provides a Root Cause Analysis (RCA) and a step-by-step Recovery Guide to help engineers fix the issue immediately.
Summary
Automated Intelligence: It’s an “IT First Responder” that converts raw system noise into precise, logical troubleshooting steps.
Context-Aware Analysis: By combining RAG with Chain-of-Thought reasoning, the system “reads the manual” for you to solve complex errors.
Data-Driven Recovery: The workflow bridges the gap between massive event logs and actionable Root Cause Analysis (RCA) to minimize downtime.
This image is an infographic that explains SCR (Short Circuit Ratio) and why it matters for AI/data center power stability. The main idea is: SCR compares grid strength at the connection point (PCC) against the data center’s load size—lower SCR means more voltage instability.
1) Top: SCR formula
SCR = Ssc / Pload
Ssc: Short-circuit MVA at the PCC → the grid’s strength / stiffness at the point where the data center connects
Pload: Rated MW of the data center load → the data center’s rated power demand
2) Middle: What high vs. low Ssc means (data center impact)
High Ssc (strong grid) → the grid can absorb sudden load changes, so voltage dips are smaller and operation is more stable.
Low Ssc (weak grid) → the same load change causes larger voltage swings, increasing the risk of trips, protection actions, or UPS transfers.
3) PCC definition (center-lower)
PCC (Point of Common Coupling) → the grid-to-data-center “handoff point” where voltage and power quality are assessed.
4) Bottom: Grid categories by SCR
Strong Grid: SCR > 3 → strong voltage support; waveform remains stable even with load fluctuations.
Weak Grid: 2 ≤ SCR < 3(shown as 3 > SCR ≥ 2 in the image) → voltage is sensitive; small load changes can cause noticeable voltage variation.
Very Weak Grid: SCR < 2 → difficult to maintain stable operation; high risk of instability or (in extreme cases) grid collapse.
summary
SCR = grid strength at PCC (Ssc) ÷ data center load (Pload).
Higher SCR means smaller voltage dips and more stable operation.