rtankihaAzaross

rtankihaAzaross is a comprehensive project that integrates AI models, a dashboard system, and automation workflows to provide a complete solution for monitoring, analyzing, and controlling systems.

Table of Contents

• Overview
• Components
• Installation Guide
  • AI Models Installation
  • Dashboard Installation
  • Automation Installation
• How It Works
  • AI Models
  • Dashboard System
  • Automation
• Integration

Overview

rtankihaAzaross combines cutting-edge AI models for face recognition and air conditioning system management with a modern dashboard interface and powerful automation workflows. This integrated system allows for efficient monitoring, control, and automation of various processes.

Components

1. AI Models:

   • Face Recognition System
   • Air Conditioning Controller
   • Maintenance Prediction System

2. Dashboard System:

   • Modern web-based interface
   • Real-time monitoring
   • Data visualization

3. Automation:

   • Node-RED based workflow automation
   • Event-driven process management
   • Integration capabilities

Installation Guide

AI Models Installation

Face Recognition System

1. Prerequisites:

   • Docker Desktop installed
   • Minimum 4GB RAM, 2 CPU cores
   • 10GB free disk space

2. Installation Steps:

   1. Install Docker Desktop
   2. Download the archive from the latest release: https://github.com/exadel-inc/CompreFace/releases
   3. Unzip the archive
   4. Run Docker
   5. Open Command prompt (write cmd in Windows search bar)
   6. Navigate to the extracted folder: cd path_of_the_folder
   7. Run command: docker-compose up -d
   8. Access the system at http://localhost:8000/login

Air Conditioning Controller & Maintenance System

1. Prerequisites:

   • Python 3.7 or higher
   • TensorFlow 2.x
   • Pandas, NumPy, Matplotlib

2. Installation Steps:

   1. Navigate to the AI model directory: cd "ai model/aircondtion controller"
   2. Install required Python packages: pip install tensorflow pandas numpy matplotlib
   3. Run the model creation script: python model.py
   4. For maintenance prediction, navigate to: cd "../maintenace"
   5. Run the maintenance model: python model.py

Dashboard Installation

1. Prerequisites:

   • Node.js (v14 or higher)
   • npm (v6 or higher)

2. Installation Steps:

   1. Navigate to the dashboard directory: cd dashboard
   2. Install npm dependencies: npm install
   3. For development mode with live preview: npm run dev
   4. For production build: npm run production
   5. Access the dashboard through the generated dist files

Automation Installation

1. Prerequisites:

   • Node.js (v14 or higher)
   • npm (v6 or higher)

2. Installation Steps:

   1. Install Node-RED globally: npm install -g --unsafe-perm node-red
   2. Navigate to the automation directory: cd automation/workflow
   3. Install dependencies: npm install
   4. Start Node-RED: node-red
   5. Access the Node-RED editor at http://localhost:1880

How It Works

AI Models

Face Recognition

The face recognition system uses deep learning models to detect, recognize, and verify faces. It provides three main services:

• Face Detection: Identifies and locates faces in images
• Face Recognition: Identifies who a person is by comparing against known faces
• Face Verification: Confirms if a face belongs to a specific person

The system is containerized using Docker for easy deployment and scaling. It exposes REST APIs for integration with other systems.

Air Conditioning Controller

This AI model uses LSTM (Long Short-Term Memory) neural networks to predict optimal air conditioning settings based on historical data. The model:

• Analyzes patterns in temperature, humidity, and usage data
• Predicts optimal settings for comfort and energy efficiency
• Provides recommendations for AC system operation

Maintenance Prediction

The maintenance prediction system uses machine learning to forecast when equipment maintenance is needed. It:

• Analyzes equipment performance data
• Identifies patterns that indicate potential failures
• Predicts maintenance needs before failures occur
• Generates maintenance schedules to prevent downtime

Dashboard System

The dashboard provides a modern, responsive interface for monitoring and controlling the entire system. Key features include:

• Real-time data visualization
• System status monitoring
• User management with role-based access control
• Configuration management for AI models and automation workflows
• Responsive design for desktop and mobile access

The dashboard is built using modern web technologies and follows best practices for UI/UX design.

Automation

The automation system is based on Node-RED, a powerful flow-based programming tool. It enables:

• Creation of automated workflows without coding
• Integration between AI models and the dashboard
• Event-driven automation based on triggers from various sources
• Scheduled tasks and processes
• Data transformation and routing

The visual programming interface makes it easy to create complex automation workflows that connect the various components of the system.

Data Center Dashboard

• A modren looking with rich features dashboard

==> instllation with

npm install --legacy-peer-deps

===> run with npm run dev

Integration

The three main components work together to provide a complete solution:

1. AI Models process data and make predictions or identifications
2. Dashboard provides visualization and user interface for the system
3. Automation connects everything together and enables workflow automation

Data flows between these components through APIs and messaging systems, creating a cohesive platform for monitoring, analysis, and control.