PI_system

(Note: All codes are there but plant_data files and input file I am not providing)

Big Picture – AI-Native Industrial Intelligence Platform

We are building an AI-Native Industrial Intelligence Platform designed to replace and outperform traditional PI (Process Information) systems such as AVEVA PI.

The platform’s core function:

• Ingest real-time and historical plant data from OPC UA, CSV, or other sources.
• Validate, process, and detect events using plant metadata (tags, assets, alarms).
• Enrich data with context from plant configuration.
• Persist it into a Neo4j Knowledge Graph (KG) for AI reasoning and analytics.

This KG acts as the central brain that AI agents, dashboards, and ML models can query for:

• Root cause analysis.
• Predictive maintenance.
• Operational optimization.

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Technical Handover – AI-Native Industrial Intelligence Platform

1. Folder Roles & Flow

d_config/ – Central Configuration & Logging

• Purpose: Houses all configuration, logging setup, and KG relation rules.
• Key Files:
  • relation_rules.yaml – Rules for linking KG entities (e.g., Asset → Alarm → TRIGGERS_ON).
  • demo_settings.py – Central settings for Neo4j, logging, data paths, and feature toggles.
• Usage: Imported by most modules to maintain a single source of truth.

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d_security/ – Authentication & Cryptography

• Purpose: Manages secure access and encryption utilities.
• Key Files:
  • demo_auth.py – Fetches credentials (env variables or vault stub).
  • demo_crypto.py – Encryption/decryption functions, with DEV and secure modes.
• Usage: Enables secure handling of sensitive plant and system data.

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plant_data/ – Plant Metadata & Demo Generators

• Purpose: Provides plant context for validating data, detecting alarms, and building the KG.
• Key Files:
  • plant_config.csv – Master tag definitions; used to validate incoming data.
  • alarm.csv – Alarm logic with thresholds, hysteresis, and priorities.
  • asset.csv – Asset definitions and hierarchy for KG asset nodes.
  • operations.py – Demo event generator for testing without live OPC UA feeds.
• Usage: Read by ingestion and KG builder modules.

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d_pipelines/ – Data Ingestion & Processing

• Purpose: Reads, validates, processes, optionally chunks, and ingests plant data.
• Key Files:
  • demo_data_reader.py – Reads CSV (long/wide), JSON, Parquet, OPC UA (mock). Filters tags via plant_config.csv.
  • demo_data_processor.py – Normalizes timestamps, coerces data types, adds schema versioning.
  • demo_data_chunker.py – Splits large datasets into smaller, manageable chunks.
  • demo_data_ingestor.py – Sends processed/chunked data to brokers (Kafka/MQTT) or to disk queues.
• Flow: Raw Data → Reader → Processor → (Chunker) → Ingestor

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KG_opc/ – Knowledge Graph Build & Persistence

• Purpose: Creates and updates the Neo4j Knowledge Graph from static metadata and dynamic events.
• Key Files:
• kg_persistor.py – Manages Neo4j transactions and validates nodes/relationships before write.
• KG_schema.yaml – Defines allowed KG entity types and relationships.
• kg_metadata.py – Loads plant_config.csv, asset.csv, alarm.csv and creates KG nodes/edges.
• ner_extractor.py – Uses spaCy to identify entities from event descriptions.
• relation_extractor.py – Applies relation_rules.yaml to infer KG relationships.
• Flow: Metadata + Events → Metadata Loader → NER + Relation Extractor → KG Persistor (Neo4j)

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2. Individual File Explanations & Key Features

d_config

• relation_rules.yaml

• Declarative mapping of entity-to-entity relations.

• Keeps relationship logic out of code.

• demo_settings.py

• One-stop configuration import for the entire pipeline.

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d_security

• demo_auth.py Pluggable credential source (env/vault). Simplifies switching between DEV and PROD modes.

• demo_crypto.py Encryption wrapper with pass-through in DEV mode. Ready for integration with enterprise security modules.

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plant_data

• plant_config.csv Primary tag reference; ensures ingestion only processes known tags.
• alarm.csv Defines alarm triggers; used in event detection.
• asset.csv Links tags to physical assets in KG.
• operations.py Creates synthetic operations/events for testing ingestion and KG population.

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d_pipelines

• demo_data_reader.py Auto-detects file format. Validates tags against plant config.

• demo_data_processor.py Cleans and normalizes incoming readings. Adds metadata for traceability.

• demo_data_chunker.py Reduces memory load in large batch processing.

• demo_data_ingestor.py Sends batches to Kafka/MQTT or to a local retry queue.

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KG_opc

• kg_persistor.py Transaction-safe writes to Neo4j. Uses MERGE for idempotent persistence.

• KG_schema.yaml Enforces KG schema consistency.

• kg_metadata.py Reads static CSVs to create plant structure in KG.

• ner_extractor.py Extracts entities to link events to assets and systems.

• relation_extractor.py Applies domain-specific rules to form semantic connections.

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3. End-to-End Data Movement

Step-by-Step

1. Input Reading Source: CSV, JSON, Parquet, or OPC UA (mock/live). demo_data_reader.py validates tags against plant_config.csv.

2. Processing demo_data_processor.py normalizes timestamps, values, and metadata.

3. Chunking (Optional) demo_data_chunker.py splits datasets for efficient handling.

4. Ingestion demo_data_ingestor.py pushes data to brokers or local queue.

5. Alarm Detection Orchestrator checks readings against alarm.csv thresholds. Generates events when limits exceeded.

6. KG Construction kg_metadata.py loads plant structure from CSVs. Events pass through ner_extractor.py and relation_extractor.py. kg_persistor.py writes final graph structure to Neo4j.

7. Alarm & Asset Integration asset.csv ensures events are tied to physical assets. alarm.csv ensures KG includes alarm nodes linked to triggered events.

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4. Combined Flow Diagram

[CSV / OPC UA] → demo_data_reader.py (validate tags) → demo_data_processor.py (normalize) → (optional) demo_data_chunker.py → demo_data_ingestor.py (broker/disk)

Alarm.csv → used for event detection Asset.csv → used for KG asset nodes

Detected Events + Plant Metadata → ner_extractor.py & relation_extractor.py → kg_persistor.py (Neo4j)

Product Overview & Roadmap – AI-Native Industrial Intelligence Platform

4. Achievements & Immediate Next Steps

✅ Achievements to Date

• Data Ingestion Pipeline

  • Reads CSV, JSON, Parquet, and mock OPC UA streams.
  • Validates tags against plant configuration to ensure integrity.

• Processing & Alarm Logic

  • Normalizes timestamps and values.
  • Detects alarms in real-time based on alarm.csv definitions.

• Knowledge Graph Foundation

  • Static plant structure (assets, tags, alarms) built into Neo4j.
  • Rules-based relationship creation using relation_rules.yaml.

• Security & Config

  • Centralized config (demo_settings.py) and rules.
  • Auth & crypto stubs ready for secure integration.

⏭ Immediate Next Steps

• Integrate real OPC UA connection instead of mock mode.
• Automate alarm event creation directly into KG.
• Enable broker-based event streaming (Kafka/MQTT) in live mode.
• Build initial query layer for KG (NLP to Cypher).

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5. Why Our Foundation is Better Than Traditional PI Systems

Feature	Our Platform	Legacy PI Systems
Data Model	Graph-based, semantic, AI-friendly	Time-series only, siloed
Context Awareness	Links sensors to assets, systems, and alarms	Raw tag values with limited metadata
Extensibility	AI agents, dashboards, ML pipelines	Closed, vendor-locked
Data Integration	Multi-format ingest + real-time OPC UA	Primarily vendor-specific connectors
Open Standards	Uses Neo4j, YAML, CSV, Python	Proprietary storage and schema

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6. Pros & Cons

Pros	Cons (to be resolved)
Flexible KG schema allows semantic reasoning	KG not yet populated with live streaming data
Modular architecture, easy to extend	No production-grade broker integration yet
AI-ready context for causal analysis	No UI/dashboard built yet
Works with both historical & real-time inputs	Alarm events not fully automated in KG updates

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7. Future Development – Bird’s Eye View

Once the KG foundation is complete and live data flows in, the platform will evolve into a full AI-powered plant intelligence system:

1. AI Chatbot Interface

   • Natural language query: “What caused Boiler 2 shutdown last week?”
   • Backed by NLP-to-Cypher translation.

2. Interactive Dashboard

   • Live plant metrics.
   • Asset health visualization.
   • Alarm/event timeline.

3. Predictive Analytics

   • ML models (TS2Vec, LSTM, Prophet) trained on historical data.
   • Predict failures before they happen.

4. Prescriptive Recommendations

   • AI agent suggests corrective actions.
   • Example: “Replace Fan 3 within 48 hours to avoid outage.”

5. Enterprise Integration

   • Role-based access control.
   • Integration with ERP/MES for work orders.

6. Self-Learning System

   • Feedback loop from operators to improve alarm rules & predictions.

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Summary Vision

Our trajectory:

• Now: Ingestion + KG foundation.
• Next: Live alarms into KG + broker integration.
• Later: AI agents, dashboards, ML-based prediction & recommendation.

This approach ensures we surpass traditional PI systems by combining semantic context, AI reasoning, and predictive capabilities into one open, extensible platform.

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