🧠 Noc-netIntel – AI-Powered Network Operations Intelligence Assistant

Noc-netIntel is an intelligent chat-driven platform designed to forecast critical network outages, provide possible root cause explanations, suggest proactive FME (Field Maintenance Engineer) deployment, and recommend resolutions — all powered by advanced AI, NLP, and time-series forecasting.

💡 What It Does

• 🔮 Predicts outages today, tomorrow, and over the week
• 📉 Identifies root causes using LSTM + LLM reasoning
• 📅 Schedules field engineers proactively
• 🧠 Suggests resolutions from a growing knowledge base
• 💬 Conversational interface with LLM (DeepSeek/OpenAI-compatible)

🛠 Technology Stack

Layer	Tech
AI/NLP	DeepSeek / Custom LLM
ML/Forecasting	PyTorch + Custom LSTM
Backend	Python (FastAPI preferred)
Frontend	JavaScript (React recommended)
Database	PostgreSQL
Data Pipeline	Python Scripts / Celery Tasks
Deployment	Docker & Docker Compose
Scheduler (optional)	Celery + Redis for task management

🧬 End-to-End Workflow

1. 🔗 Data Collection

• Sources: Sensor logs, BTS data, voltage/current levels, historical tickets, alarms
• Stored in PostgreSQL (structured) and optional object storage (raw logs)

2. 🧹 Data Preprocessing

• Cleansing missing/nulls, noise filtering
• Timestamp alignment, interpolation
• Scaling, encoding categorical signals (battery status, alarm type)

3. 🔧 Feature Engineering

• Temporal signals: time of day, day of week, holiday
• Environmental: power metrics, weather (optional)
• Historical: frequency of past outages, lag features
• Rolling stats: moving average, rate of failure

4. 📊 ML Forecasting (PyTorch + LSTM)

• Input: Sequence of multivariate time series
• Architecture: Multi-head LSTM → Dense heads (classification + regression)
• Outputs:
  • Outage probability
  • Affected region/site
  • Possible root cause embeddings
• Metrics: F1, AUC for classification; RMSE for regression

5. 🧠 NLP Reasoning Layer (DeepSeek / LLM)

• Converts ML output into readable advice
• Enhances with historical patterns and predefined rules
• Formats chat response: outage + root cause + FME plan + resolution

6. 📅 Proactive FME Scheduler

• Ranks urgency and location clustering
• Optimizes FME routing using heuristic or ML-based dispatch
• Integrates with external calendars/ticketing if needed

💬 Sample Chat Interaction

User: "What outages are expected tomorrow in the North East zone?"
Noc-netIntel:

🛑 Predicted 3 possible outages:

• Site BGH-29 (Power drain) – 87% chance
• Site TMT-02 (Overload) – 72% chance
• Site JAK-10 (Backhaul degradation) – 55% chance

📌 Root Causes: Battery degradation, high load demand, backhaul link instability 🛠 Recommended Actions: Pre-deploy backup power units, initiate remote checks 👷 FME Suggestion: Team Alpha, report at 06:30 AM

🚀 API Overview

• POST /chat – Accepts user prompt, returns AI-generated insight
• GET /forecast – Returns raw model prediction
• GET /schedule – Lists recommended FME deployments
• GET /logs – Access recent outage logs (if allowed)

Full Swagger UI at: http://localhost:8000/docs

🗃 Sample PostgreSQL Schema

CREATE TABLE outage_forecasts ( id SERIAL PRIMARY KEY, site_code TEXT, prediction_date TIMESTAMP, outage_probability FLOAT, root_cause TEXT, fme_plan TEXT, resolution TEXT );

🐳 Setup and Deployment

✅ Prerequisites

• Docker & Docker Compose
• Python 3.9+
• Node.js (for frontend)

📦 Running Locally

bash git clone https://github.com/moses000/mysoftware-nocNetIntel cd noc-netintel

Run with Docker Compose

docker-compose up --build

Services:

• backend: FastAPI ML/NLP engine
• frontend: React chat UI (optional)
• ml_worker: PyTorch + model runner
• postgres: SQL data store

🔐 Auth & Roles

• JWT-based auth
• Roles: Admin, Analyst, Engineer
• Granular data access policies

📊 Monitoring & Logging

• Optional: Add Grafana for real-time alert visualization
• Backend logs all predictions and user queries
• Alerts for model drift / threshold breaches

✍️ Wiki & Docs

• 📘 docs/data-pipeline.md: Ingestion, ETL, transformations
• 📘 docs/model.md: LSTM architecture, training notes
• 📘 `docs