## 🌍 Project Intro **ResqNet** is an advanced, real-time emergency management and coordination system designed to bridge the gap between distressed individuals, first responders, and command centers during critical disaster events. By leveraging real-time geospatial tracking, AI-assisted incident analysis, and high-availability websocket communications, ResqNet empowers teams to make data-driven decisions when every second counts.

## 📸 Screenshots | Dashboard Overview | Live Tracking Map | |:---:|:---:| | | | | **Command Center Analytics** | **AI Assistant & Incident Triage** | | | | ## 🏗️ Architecture Diagrams ResqNet uses a distributed architecture to ensure high availability and low latency during peak traffic (disaster events). ### System Flow Diagram graph TD User([End User / Victim]) -->|SOS Signal| Client(ResqNet Client / PWA) Client -->|REST / WSS| LoadBalancer[Load Balancer] LoadBalancer --> API[API Gateway] LoadBalancer --> WSS[WebSocket Server] API --> Services[Backend Microservices] WSS --> PubSub[(Redis Pub/Sub)] Services --> DB[(PostgreSQL)] Services --> ML[AI Triage Engine] PubSub --> WSS WSS --> Responder([First Responder Terminal]) ### Backend Services Diagram graph LR Gateway[API Gateway] --> Auth[Auth Service] Gateway --> Incident[Incident Service] Gateway --> Geo[Geo-Spatial Service] Gateway --> Shelter[Shelter Management] Incident --> DB[(Primary DB)] Geo --> Redis[(Cache & Location)] Incident --> Kafka[Event Bus] ### WebSocket Flow (Real-Time Communication) sequenceDiagram participant C as Client (Victim) participant W as WebSocket Node participant R as Redis Pub/Sub participant D as Dispatcher UI C->>W: Connect (Token) W-->>C: Ack & Subscribe C->>W: Emit 'location_update' {lat, lng} W->>R: Publish 'incident:123' R->>W: Fan-out to Subscribers W->>D: Push 'location_update' D-->>W: Acknowledge ## ✨ Features - **Real-Time Incident Dashboard**: Bird's-eye view of all active incidents, severity levels, and resource allocations. - **Geospatial Tracking (Map)**: Live tracking of responders, assets, and SOS signals on an interactive 3D map. - **Advanced Analytics**: Post-incident reports, response time heatmaps, and resource utilization metrics. - **AI Assistant**: NLP-based triage system that prioritizes incidents based on incoming distress text/audio. - **Shelter Management System**: Real-time capacity tracking, inventory management, and routing for displaced persons. - **Offline Capabilities**: Core functionalities are cached via PWA for intermittent connectivity zones. ### 🧪 Simulated Features *To demonstrate the full potential of the architecture without requiring hardware integrations, the following features are simulated within the platform:* - **Drone Feeds**: Pre-recorded aerial footage fed through the streaming pipeline. - **Satellite Monitoring**: Historical thermal anomaly data replayed as real-time events. - **Blockchain Logging**: Simulated immutable ledger transactions for audit trails. - **Thermal AI**: Mocked object detection bounding boxes on thermal video feeds. ## 🛠️ Tech Stack **Frontend:** - React (Vite) / Next.js - Tailwind CSS - Mapbox GL JS / Deck.gl - Socket.io-client / Zustand **Backend:** - Node.js (Express / NestJS) - PostgreSQL (PostGIS) - Redis (Pub/Sub & Caching) - Socket.io - Prisma ORM **Infrastructure (Simulated/Planned):** - Docker & Kubernetes - AWS (EC2, S3, RDS) - NGINX ## 🚀 Installation & Setup 1. **Clone the repository** git clone https://github.com/Rishisharma029/resqnet.git cd resqnet 2. **Environment Variables** cp .env.example .env # Update .env with your specific keys (DB, Mapbox, etc.) 3. **Install Dependencies** # Install server dependencies cd server npm install # Install client dependencies cd ../client npm install 4. **Run the Application** # In terminal 1 (Backend) cd server npm run dev # In terminal 2 (Frontend) cd client npm run dev ## 🗺️ Roadmap - [x] Phase 1: Core authentication and basic SOS routing. - [x] Phase 2: Real-time map integration and WebSocket setup. - [ ] Phase 3: AI Assistant NLP integration for automatic triage. - [ ] Phase 4: Mobile App (React Native) deployment for offline-first usage. - [ ] Phase 5: Hardware integration (IoT sensors, actual drone feeds).

标签：自定义脚本