Bon Voyage 🚢

Bon Voyage is an AI-powered maritime voyage management system that provides fuel consumption prediction, route optimisation, and maintenance forecasting for ships.

Features

• 🤖 AI-Powered Predictions: Machine learning models for fuel consumption and route optimisation
• ⛽ Fuel Management: Track and predict fuel usage across voyages
• 🗺️ Route Optimisation: Find optimal routes considering weather and cargo
• 🔧 Maintenance Forecasting: Predictive maintenance scheduling
• 📊 Voyage Analytics: Comprehensive voyage tracking and feedback
• 👥 User Management: Authentication and user-specific data

Tech Stack

• Backend: Node.js, Express, TypeScript
• Database: PostgreSQL with Prisma ORM
• AI/ML: TensorFlow.js
• Containerization: Docker & Docker Compose
• Package Manager: pnpm

Prerequisites

Before setting up the application, ensure you have the following installed:

• Docker (v20.0+)
• Docker Compose (v2.0+)
• Git

Note: No need to install Node.js, pnpm, or PostgreSQL locally as they run inside Docker containers.

Quick Start

1. Clone the Repository

git clone <your-repository-url>
cd bon-voyage

2. Start the Application

docker-compose up --build

This single command will:

• Build the application Docker image
• Start PostgreSQL database
• Run database migrations
• Seed the database with sample data
• Train AI models
• Start the Express server

3. Access the Application

Once the setup is complete, you'll see:

🚢 Server is running on port 4000

The API will be available at: http://localhost:4000

API Endpoints

Authentication

• POST /auth/register - Register a new user
• POST /auth/login - Login user

Voyages

• POST /voyage/plan-voyage - Create a new voyage and predict the details

Plan History

• GET /voyage/plan-history - Compare actual and predicted data for all voyages
• GET /voyage/plan-history/:voyageId - Compare actual and predicted data for a specific voyage

Maintenance

• GET /maintenance-alerts/:shipId - Get maintenance predictions

Feedback

• POST /feedback/provide-feedback - Submit voyage feedback

API Docs [Postman]

• Postman publised documentation for all the APIS

Development Setup

For development with hot-reload and local debugging:

1. Install Dependencies Locally

bash

Install Node.js (v18+) and pnpm

npm install -g pnpm
pnpm install

2. Setup Environment Variables

Create a .env file in the root directory:

env

DATABASE_URL="postgresql://postgres:password123@localhost:5432/bon_voyage"
DIRECT_URL="postgresql://postgres:password123@localhost:5432/bon_voyage"
PORT=3000
JWT_SECRET="your-secret-key-here"

3. Run Database Migrations and Seed

pnpm db:push
pnpm db:generate
pnpm seed

4. Start Development Server

pnpm dev

Available commands

# Development
pnpm dev              # Start development server with nodemon
pnpm build            # Build TypeScript to JavaScript
pnpm start            # Start production server

# Database
pnpm db:generate      # Generate Prisma client
pnpm db:push          # Push schema to database
pnpm db:migrate       # Run migrations
pnpm db:reset         # Reset database
pnpm db:studio        # Open Prisma Studio
pnpm seed             # Seed database with sample data

# AI/ML
pnpm train            # Train AI models manually

Docker Commands

# Start all services
docker-compose up

# Rebuild and start
docker-compose up --build

# Stop all services
docker-compose down

# View logs
docker-compose logs app
docker-compose logs postgres

Database Schema

The application uses the following main entities:

• Users: Authentication and user management
• Ships: Fleet management
• Voyages: Trip planning and tracking
• FuelLogs: Fuel consumption tracking
• VoyageFeedback: Post-voyage analytics
• MaintenanceRecords: Equipment maintenance tracking

AI Models

The application includes three AI models that are automatically trained on startup:

1. Fuel Predictor: Predicts fuel consumption based on distance, cargo weight, weather conditions
2. Route Optimizer: Optimizes routes considering weather and cargo constraints
3. Maintenance Forecaster: Predicts maintenance needs based on usage patterns

Container Issues (I personally faced these problems)

-Problem: exec ./docker-entrypoint.sh: no such file or directory -Solution: This is usually a line-ending issue. The Dockerfile includes dos2unix to handle this automatically.

-Problem: Cannot find module '/app/dist/server.js' -Solution: Ensure you're not using volume mounts that override the built image in production.

-Problem: The table 'public.voyages' does not exist -Solution: Run database migrations:

docker-compose exec app pnpm db:push

-Problem: Connection refused to PostgreSQL -Solution: Ensure PostgreSQL container is running:

docker-compose up postgres -d

-Problem: Slow AI model training -Solution: The app includes TensorFlow.js CPU backend. For faster training, you can:

• Use a machine with more CPU cores
• The models train once on startup and save to disk

How my system supports planning intelligence

This Project, Bon Voyage works with AI/ML to provide data oriented decision support for Marine Operations.

• Planning the voyage & Fuel Prediction:

  the /plan-voyage optimal speed and fuel consumption based on cargo weight, distance, and weather. This allows operators to plan cost-efficient and timely routes. A regression model forecasts fuel usage, enabling better refueling decisions and cost management.

• Route Optimization:

  The system estimates voyage duration and recommends a dynamic speed schedule across segments, improving ETA predictions and voyage efficiency.

• Maintenance Forecasting:

  Based on historical fuel logs and maintenance records, the system predicts the next due maintenance date, readiness time, and health score for each ship.

• Continuous learning loop for more accurate datas:

  With /feedback endpoints and model retraining schedules, the system evolves using real operational data, improving over time with usage.

• Added AIModel:

  For savimg each training model's version, accuracy and metadata for reliability.

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Happy Sailing! ⛵