README.md

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AI assistants: Read README.ai.md first for a concise, AI-friendly overview of IoT DC3.

IoT DC3 — Connect the Physical World to AI
An open-source Industrial IoT Runtime for Physical AI

https://dc3.site

🔌 Multi-protocol connectivity  ·  🤖 MCP tool gateway  ·  ☁️ Cloud-native microservices

IoT DC3 decouples devices from applications: drivers push normalized point data into a message bus, and applications consume it through one unified API — add or change devices without touching applications, and build new applications without touching devices.

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📸 Product Preview

📸 Platform Overview	📸 Device Management	📸 AI Agent
Home / Dashboard System overview · Online device metrics · Data trend charts	Device Management Device list · Online status · Search and filtering	AI Agent Assistant Natural-language device queries · Data insights · Governed execution

🏗️ Architecture Overview

Architecture at a Glance

Six-layer microservice architecture at a glance: clients → gateway → four center services → message bus → 36 protocol drivers → field devices. PostgreSQL (TimescaleDB + pgvector + AGE) persistence and optional observability stack (ELK + Prometheus + Grafana) laid out in one view.

🧱 Design principles — cross-service calls always go through Facade interfaces; the DO/BO/VO three-tier model keeps persistence, business, and API shapes strictly separated; and tenant isolation runs end to end across database, cache, and API paths. Clear boundaries that scale across services and teams.

📖 For the full architecture documentation, see System Architecture Overview.

✨ Core Features

🔌 Multi-Protocol Device Connectivity

IoT DC3 includes 36 access driver modules for industrial automation, IoT communication, data bridging, basic communication, and simulation/debugging scenarios, reducing the cost of connecting common devices and data sources:

Category	Driver Modules
🏭 Industrial protocols	Modbus TCP · Modbus RTU · OPC UA · OPC DA · Siemens S7 · BACnet/IP · EtherNet/IP · Omron FINS · Mitsubishi MELSEC · IEC 60870-5-104 · IEC 61850 · DNP3 · DLMS · DLT645 · KNX · M-Bus · SL651
📡 IoT protocols	MQTT · CoAP · LwM2M · HTTP · BLE · Zigbee · LoRaWAN
🗄️ Data bridging	MySQL · PostgreSQL · Oracle · SQL Server · Redis
🔧 Basic communication, messaging and NMS	TCP/UDP · Serial · SNMP · CAN · Kafka
🧪 Simulation and debugging	Virtual · Listening Virtual

The Driver SDK supports fast development of custom protocol drivers and registration into the runtime platform.

🤖 From Device Data to Physical AI

The Agentic Center is built on Spring AI, and the platform exposes an MCP tool gateway so AI agents can act on the physical world in one safe, controllable, traceable loop:

• MCP tool gateway — external AI agents connect through the Model Context Protocol: OAuth client registration, per-tool authorization, and a full audit trail for every tool call
• Natural-language assisted operations - through Tool Calling and under access control, LLMs can query devices, read/write points, and assist with command execution
• Intelligent alarm analysis - AI assists with root-cause analysis and response suggestions
• Data insights - Query device data in natural language and generate visual charts
• Multi-model support - Compatible with OpenAI API-style providers and mainstream models such as GPT, Claude, DeepSeek, and Qwen
• Conversation memory - Multi-turn conversations and context memory persisted to the database

🏗️ Cloud-Native Microservices

Distributed microservice architecture based on Spring Boot 4 + Spring Cloud 2025:

• Service governance - Spring Cloud Gateway as the unified entrypoint, with static routes and flexible environment variables
• Efficient communication - gRPC service calls with Protobuf serialization
• Horizontal scaling - Stateless design for scaling individual services by workload
• Resilience - Replaceable service nodes and fault isolation

📊 Real-Time Data Engine

• Data collection - Drivers collect device telemetry and send it asynchronously through the internal message broker — pluggable per deployment: RabbitMQ (default), Kafka, Pulsar or any MQTT 5 broker (broker guide)
• Time-series storage - Efficient queries for real-time and historical data
• Rule engine - Flexible alarm rules with multi-level alarms and notifications
• Event traceability - Full command and event history

🔐 Enterprise Security and Multi-Tenancy

• Tenant isolation - Tenant-level isolation across database, cache, and API paths
• Authentication and authorization - JWT + Spring Security with RBAC
• Transport encryption - TLS/SSL communication support
• Audit tracking - User operation and system event logs

🧩 Developer Friendly

• Driver SDK - A complete driver development toolkit. See the Driver Authoring Guide
• Separated frontend and backend - Vue 3 + TypeScript frontend, RESTful and gRPC APIs
• Containerized deployment - One-command startup with Podman / Docker Compose, plus compose scaling, Docker Swarm, Kubernetes and Helm deployment configs. See the Deployment Guide.
• Complete documentation - Online docs, quickstart guide, and troubleshooting guide

⚡ Quick Start

For source-based local development, start PostgreSQL and RabbitMQ, load local environment variables, then build:

make up-db
source dc3/env/dev.env.sh
mvn -s .mvn/settings.xml clean package

Use make up-db-cn if you prefer the Alibaba Cloud registry in Mainland China.

📖 For service startup order, IDE setup, verification commands, and common pitfalls, see the full Quickstart.

🛠️ Technology Stack

IoT DC3 is built on Java 21, Spring Boot 4, Spring Cloud 2025, Spring AI 2, PostgreSQL, a pluggable message broker (RabbitMQ, Kafka, Pulsar or MQTT 5 — selection guide), gRPC, Vue 3, TypeScript, and Vite.

See Technology Stack for component details and where each technology is used.

📖 Documentation and Community

Resource	Link
📚 Online docs	docs.dc3.site
🎬 Live demo	demo.dc3.site
🏭 Industry demos	dc3.site/en/demo
🚀 Quickstart	Quickstart Guide
🛠️ Technology stack	Technology Stack
🏗️ Architecture	Modules and Dependencies
🔧 Driver development	Driver Authoring Guide
🐛 Troubleshooting	Troubleshooting
📋 Changelog	Release Changelog
💰 Pricing & licensing	Plans and commercial license
🐛 Issue feedback	GitHub Issues
🇨🇳 Gitee mirror	Gitee GVP Project

🌍 Use Cases

Twelve illustrative industry dashboards — built on IoT DC3 with mock data — show how the platform lands in each scenario. Browse all demos.

🏭 Smart Factory — OEE monitoring	💧 Water Network — digital twin	⚡ Microgrid — solar-storage balance
🌾 Precision Agriculture — greenhouse climate	🏢 Smart Building — HVAC and occupancy	🚦 Smart Traffic — congestion and signals
🛢️ Oil & Gas Pipeline — line pressure	⛏️ Smart Mine — gas and ventilation	❄️ Cold Chain — temperature traceability
🌿 Environmental Monitoring — air and water quality	⚓ Smart Port — berth and yard scheduling	🔌 EV Charging — load and storage synergy

🤝 Contributing

Contributions of all kinds are welcome. Please follow this workflow:

1. Fork and branch - Create a branch from main, using the format feature/your_name/feature_description (for example: feature/pnoker/mqtt_driver)
2. Develop and commit - Complete your changes on the new branch and follow the Conventional Commits specification
3. Open a PR - Submit a Pull Request to the develop branch for maintainer review and merge

📄 License

IoT DC3 is open source under the AGPL 3.0 license.

• ✅ Personal learning, research, and internal use - Free
• ✅ Modify the code and open source your changes - Welcome
• ⚠️ Offering it as a commercial service to third parties without open-sourcing modifications - Requires a commercial license

For commercial licensing details, see LICENSE.txt.