# easyaiot **Repository Path**: volara/easyaiot ## Basic Information - **Project Name**: easyaiot - **Description**: 我希望全世界都能使用这个系统，实现AI的真正0门槛，人人都能体验到AI带来的好处，而并不只是掌握在少数人手里。支持上千种垂直场景，支持AI模型定制化和AI算法定制化开发深度融合，赋能万物智视：EasyAIoT 构筑了物联网设备（尤其是海量摄像头）的高效接入与管控网络。我们深度融合流媒体实时传输技术与前沿人工智能（AI），打造一体化服务核心。 - **Primary Language**: Java - **License**: Apache-2.0 - **Default Branch**: main - **Homepage**: None - **GVP Project**: Yes ## Statistics - **Stars**: 1075 - **Forks**: 310 - **Created**: 2025-08-06 - **Last Updated**: 2025-12-14 ## Categories & Tags **Categories**: iot **Tags**: None ## README # EasyAIoT (Cloud-Edge-Device Integrated Collaborative Algorithm Application Platform) [![Gitee star](https://gitee.com/volara/easyaiot/badge/star.svg?theme=gvp)](https://gitee.com/soaring-xiongkulu/easyaiot/stargazers) [![Gitee fork](https://gitee.com/volara/easyaiot/badge/fork.svg?theme=gvp)](https://gitee.com/soaring-xiongkulu/easyaiot/members)

My vision is for this system to be accessible worldwide, achieving truly zero barriers to AI. Everyone should experience the benefits of AI, not just a privileged few.

English | 简体中文 | 繁體中文 | Русский | Français | 한국어

## 🌟 Some Thoughts on the Project ### 📍 Project Positioning

EasyAIoT is a cloud-edge-device integrated intelligent IoT platform that focuses on the deep integration of AI and IoT. Through core capabilities such as algorithm task management, real-time stream analysis, and model service cluster inference, the platform achieves a complete closed-loop from device access to data collection, AI analysis, and intelligent decision-making, truly realizing interconnected everything and intelligent control of everything.

#### 🧠 AI Capabilities

Vision Large Model Intelligent Understanding: Integrated with QwenVL3 vision large model, supports deep visual reasoning and semantic understanding of real-time video frames, enabling intelligent analysis and scene comprehension of frame content, providing richer visual cognitive capabilities, achieving a leap from pixel-level perception to semantic-level understanding
Real-Time Camera AI Analysis: Supports AI intelligent analysis of real-time camera feeds. Performs AI algorithm processing such as object detection, behavior analysis, and anomaly recognition on real-time video streams, providing millisecond-level response real-time analysis results, supporting concurrent analysis of multiple video streams
Cloud-Edge-Device Integrated Algorithm Alert Monitoring Dashboard: Provides a unified cloud-edge-device integrated algorithm alert monitoring dashboard that displays key information in real-time, including device status, algorithm task operations, alarm event statistics, and video stream analysis results. Supports multi-dimensional data visualization, achieving unified monitoring and management of cloud, edge, and device layers, providing decision-makers with a global perspective intelligent monitoring command center
Device Detection Region Drawing: Provides a visual device detection region drawing tool that supports drawing rectangular and polygonal detection regions on device snapshot images, supports flexible association configuration between regions and algorithm models, supports visual management, editing, and deletion of regions, supports keyboard shortcuts to improve drawing efficiency, enabling precise region detection configuration and providing accurate detection range definitions for algorithm tasks
Intelligent Linked Alert Mechanism: Supports a triple-link mechanism between detection regions, defense time periods, and event alerts. The system intelligently determines whether a detected event simultaneously meets the specified detection region range, falls within the defense time period, and matches the alert event type. Alerts are only triggered when all three conditions are met, achieving precise spatiotemporal condition filtering, significantly reducing false positive rates, and improving the accuracy and practicality of the alert system
Large-Scale Camera Management: Supports access to hundreds of cameras, providing end-to-end services including collection, annotation, training, inference, export, analysis, alerting, recording, storage, and deployment
Algorithm Task Management: Supports creation and management of two types of algorithm tasks, each task can flexibly bind frame extractors and sorters to achieve precise video frame extraction and result sorting
- Real-Time Algorithm Tasks: Used for real-time video analysis, supporting RTSP/RTMP stream real-time processing with millisecond-level response capabilities, suitable for monitoring, security, and other real-time scenarios
- Snapshot Algorithm Tasks: Used for snapshot image analysis, performing intelligent recognition and analysis on captured images, suitable for event backtracking, image retrieval, and other scenarios
Stream Forwarding: Supports direct viewing of camera real-time feeds without enabling AI analysis functionality. By creating stream forwarding tasks, multiple cameras can be batch-pushed, enabling synchronous viewing of multiple video streams to meet pure video monitoring scenario requirements
Model Service Cluster Inference: Supports distributed model inference service clusters, achieving intelligent load balancing, automatic failover, and high availability guarantees, significantly improving inference throughput and system stability
Defense Time Period Management: Supports two defense strategies: full defense mode and half defense mode, allowing flexible configuration of defense rules for different time periods, achieving precise time-based intelligent monitoring and alerting
OCR and Speech Recognition: High-precision text recognition based on PaddleOCR with speech-to-text functionality, providing multi-language recognition capabilities
Multimodal Vision Large Models: Supports various vision tasks including object recognition and text recognition, providing powerful image understanding and scene analysis capabilities
LLM Large Language Models: Supports intelligent analysis and understanding of multiple input formats including RTSP streams, video, images, audio, and text, achieving multimodal content understanding
Model Deployment and Version Management: Supports rapid deployment and version management of AI models, enabling one-click model deployment, version rollback, and gray release
Multi-Instance Management: Supports concurrent operation and resource scheduling of multiple model instances, improving system utilization and resource efficiency
Camera Snapshot: Supports real-time camera snapshot functionality with configurable snapshot rules and trigger conditions, achieving intelligent snapshot capture and event recording
Snapshot Storage Space Management: Provides storage space management for snapshot images with quota and cleanup policy support, ensuring rational utilization of storage resources
Video Storage Space Management: Provides storage space management for video files with automatic cleanup and archiving, achieving intelligent storage resource management
Snapshot Image Management: Supports full lifecycle management of snapshot images including viewing, searching, downloading, and deletion, providing convenient image management functionality
Device Directory Management: Provides hierarchical device directory management with device grouping, multi-level management, and permission control, achieving organized and fine-grained device management
Alarm Recording: Supports automatic recording triggered by alarm events. When abnormal events are detected, relevant video clips are automatically recorded, providing a complete alarm evidence chain. Supports viewing, downloading, and management of alarm recordings
Alarm Events: Provides comprehensive alarm event management functionality, supporting real-time alarm event push, historical query, statistical analysis, event processing, and status tracking, achieving full lifecycle management of alarms
Video Playback: Supports fast retrieval and playback of historical recordings, providing convenient operations such as timeline positioning, variable speed playback, and keyframe jumping. Supports synchronized playback of multiple video streams, meeting event backtracking and analysis needs

#### 🌐 IoT Capabilities

Device Access and Management: Device registration, authentication, status monitoring, lifecycle management
Product and Thing Model Management: Product definition, thing model configuration, product management
Multi-Protocol Support: Multiple IoT protocols including MQTT, TCP, HTTP
Device Authentication and Dynamic Registration: Secure access, identity authentication, dynamic device registration
Rule Engine: Data flow rules, message routing, data transformation
Data Collection and Storage: Device data collection, storage, query, and analysis
Device Status Monitoring and Alert Management: Real-time monitoring, anomaly alerts, intelligent decision-making
Notification Management: Supports 7 notification methods including Feishu, DingTalk, Enterprise WeChat, Email, Tencent Cloud SMS, Alibaba Cloud SMS, and Webhook, enabling flexible and multi-channel alert notifications

### 💡 Technical Philosophy

We believe no single programming language excels at everything, but through the deep integration of three programming languages, EasyAIoT leverages the strengths of each to build a powerful technical ecosystem.

Java excels at building stable and reliable platform architectures but is not suitable for network programming and AI development; Python excels at network programming and AI algorithm development but has bottlenecks in high-performance task execution; C++ excels at high-performance task execution but is less suitable than the other two for platform development and AI programming. EasyAIoT adopts a tri-lingual mixed programming architecture, fully leveraging the strengths of each language to build an AIoT platform that's challenging to implement but extremely easy to use.

![EasyAIoT Platform Architecture.jpg](.image/iframe2.jpg) ### 🔄 Module Data Flow

### 🤖 Zero-Shot Labeling Technology

Innovatively leveraging large models to construct a zero-shot labeling technical system (ideally completely eliminating manual labeling, achieving full automation of the labeling process), this technology generates initial data through large models and completes automatic labeling via prompt engineering. It then ensures data quality through optional human-machine collaborative verification, thereby training an initial small model. This small model, through continuous iteration and self-optimization, achieves co-evolution of labeling efficiency and model accuracy, ultimately driving continuous improvement in system performance.

### 🏗️ Project Architecture Features

EasyAIoT is not actually one project; it is seven distinct projects.

What's the benefit? Suppose you are on a resource-constrained device (like an RK3588). You can extract and independently deploy just one of those projects. Therefore, while this project appears to be a cloud platform, it simultaneously functions as an edge platform.

🌟 Genuine open source is rare. If you find this project useful, please star it before leaving - your support means everything to us!
(In an era where fake open-source projects are rampant, this project stands out as an exception.)

### 🌍 Localization Support

EasyAIoT actively responds to localization strategies, providing comprehensive support for localized hardware and operating systems, delivering secure and controllable AIoT solutions for users:

🖥️ Server-Side Support

Full compatibility with Hygon x86 architecture processors
Support for localized server hardware platforms
Targeted performance optimization solutions
Ensures stable operation of enterprise applications

📱 Edge-Side Support

Comprehensive support for Rockchip ARM architecture chips
Perfect adaptation to mainstream edge computing platforms like RK3588
Deep optimization for edge scenarios
Enables lightweight deployment of edge intelligence

🖱️ Operating System Support

Compatible with Kylin operating system
Support for localized Linux distributions like Founder
Adaptation to mainstream localized operating systems like UOS
Provides complete localized deployment solutions

## 🎯 Application Scenarios ![Application Scenarios.png](.image/适用场景.png) ## 🧩 Project Structure

EasyAIoT consists of five core projects:

Module	Description
WEB Module	Frontend management interface based on Vue, providing a unified user interaction experience
DEVICE Module	Technical Advantages: Based on JDK21, providing better performance and modern features Device Management: Device registration, authentication, status monitoring, lifecycle management Product Management: Product definition, thing model management, product configuration Protocol Support: Multiple IoT protocols including MQTT, TCP, HTTP Device Authentication: Device dynamic registration, identity authentication, secure access Rule Engine: Data flow rules, message routing, data transformation Data Collection: Device data collection, storage, query, and analysis
VIDEO Module	Stream Processing: Supports RTSP/RTMP stream real-time processing and transmission Algorithm Task Management: Supports real-time algorithm tasks and snapshot algorithm tasks, used for real-time video analysis and snapshot image analysis respectively Frame Extractor and Sorter: Supports flexible frame extraction strategies and result sorting mechanisms, each algorithm task can bind independent frame extractors and sorters Defense Time Period: Supports time-based configuration for full defense mode and half defense mode
AI Module	Intelligent Analysis: Responsible for video analysis and AI algorithm execution Model Service Cluster: Supports distributed model inference services, achieving load balancing and high availability Real-Time Inference: Provides millisecond-level response real-time intelligent analysis capabilities Model Management: Supports model deployment, version management, and multi-instance scheduling
TASK Module	C++-based high-performance task processing module responsible for compute-intensive task execution

## 🖥️ Cross-Platform Deployment Advantages

EasyAIoT supports deployment on Linux, Mac, and Windows, providing flexible and convenient deployment solutions for users in different environments:

🐧 Linux Deployment Advantages

Ideal for production environments, stable and reliable with low resource consumption
Supports Docker containerized deployment with one-click service startup
Perfect compatibility with servers and edge computing devices (such as RK3588 and other ARM architecture devices)
Provides complete automated installation scripts to simplify deployment

🍎 Mac Deployment Advantages

Suitable for development and testing environments with deep macOS integration
Supports local development and debugging for rapid feature validation
Provides convenient installation scripts compatible with package managers like Homebrew

🪟 Windows Deployment Advantages

Suitable for Windows server environments, reducing learning curve
Supports PowerShell automation scripts to simplify deployment operations
Compatible with both Windows Server and desktop Windows systems
Provides graphical installation wizards for user-friendly experience

Unified Experience: Regardless of the operating system chosen, EasyAIoT provides consistent installation scripts and deployment documentation, ensuring a uniform cross-platform deployment experience.

## ☁️ EasyAIoT = AI + IoT = Cloud-Edge Integrated Solution

Supports thousands of vertical scenarios with customizable AI models and algorithm development.

Deep Integration Empowers Intelligent Vision for Everything

EasyAIoT constructs an efficient access and management network for IoT devices (especially massive cameras). We deeply integrate real-time streaming technology with cutting-edge AI to create a unified service core. This solution not only enables interoperability across heterogeneous devices but also deeply integrates HD video streams with powerful AI analytics engines, giving surveillance systems "intelligent eyes" – accurately enabling facial recognition, abnormal behavior analysis, risk personnel monitoring, and perimeter intrusion detection.

The platform supports two types of algorithm tasks: real-time algorithm tasks for real-time video analysis of RTSP/RTMP streams, providing millisecond-level response capabilities; snapshot algorithm tasks for intelligent analysis of captured images, supporting event backtracking and image retrieval. Through algorithm task management, flexible frame extraction and sorting strategies are achieved, with each task able to bind independent frame extractors and sorters. Combined with model service cluster inference capabilities, millisecond-level response and high availability are ensured. Additionally, two defense strategies are provided: full defense mode and half defense mode, allowing flexible configuration of monitoring rules for different time periods, achieving precise time-based intelligent monitoring and alerting.

In terms of IoT device management, EasyAIoT provides comprehensive device lifecycle management capabilities, supporting multiple IoT protocols (MQTT, TCP, HTTP) to achieve rapid device access, secure authentication, real-time monitoring, and intelligent control. Through the rule engine, intelligent data flow and processing of device data are realized, combined with AI capabilities for in-depth analysis of device data, achieving full-process automation from device access, data collection, intelligent analysis to decision execution, truly realizing interconnected everything and intelligent control of everything.

## ⚠️ Disclaimer EasyAIoT is an open-source learning project unrelated to commercial activities. Users must comply with laws and regulations and refrain from illegal activities. If EasyAIoT discovers user violations, it will cooperate with authorities and report to government agencies. Users bear full legal responsibility for illegal actions and shall compensate third parties for damages caused by usage. All EasyAIoT-related resources are used at the user's own risk. ## 📚 Deployment Documentation - [Platform Deployment Documentation](.doc/部署文档/平台部署文档.md) ## 🎮 Demo Environment - Demo URL: http://36.111.47.113:8888/ - Username: admin - Password: admin123 ## ⚙️ Project Repositories - Gitee: https://gitee.com/soaring-xiongkulu/easyaiot - Github: https://github.com/soaring-xiongkulu/easyaiot ## 📸 Screenshots

## 🛠️ Service Support

We offer various service options to help you gain an in-depth understanding of the EasyAIoT platform and its code. Through product documentation, technical exchange groups, paid tutorials, and more, you will receive the following services:

Service Item	Service Content	Service Fee	Service Method
System Deployment	Complete EasyAIoT deployment in the customer-specified network and hardware environment	¥500	Online Deployment Support
Technical Support	Provide solutions for issues encountered during deployment and functional usage	¥200	Online Remote Support within 30 Minutes
Other Services	Customized development for vertical industry solutions; tailored duration and functional services	Negotiable	Negotiable

## 📞 Contact Information (After adding WeChat, please follow the official account to join the technical exchange group)

## 👥 Official Account

## 🪐 Knowledge Planet:

知识星球

## 💰 Sponsorship

## 🤝 Contributing

We welcome all forms of contributions! Whether you are a code developer, documentation writer, or issue reporter, your contribution will help make EasyAIoT better. Here are the main ways to contribute:

💻 Code Contribution

Fork the project to your GitHub/Gitee account
Create a feature branch (git checkout -b feature/AmazingFeature)
Commit your changes (git commit -m 'Add some AmazingFeature')
Push to the branch (git push origin feature/AmazingFeature)
Open a Pull Request

📚 Documentation Contribution

Improve existing documentation content
Add usage examples and best practices
Provide multilingual translations
Fix documentation errors

🌟 Other Ways to Contribute

Report and fix bugs
Suggest feature improvements
Participate in community discussions and help other developers
Share usage experiences and case studies

## 🌟 Major Contributors

The following are outstanding contributors who have made significant contributions to the EasyAIoT project. Their contributions have played a key role in promoting the project's development. We express our most sincere gratitude!

Contributor	Contribution
℡夏别	Contributed Windows deployment documentation for the EasyAIoT project, providing a complete deployment guide for Windows platform users, greatly reducing the deployment difficulty in Windows environments, and enabling more users to easily use the EasyAIoT platform.
YiYaYiYaho	Contributed Mac container one-click deployment script for the EasyAIoT project, providing an automated deployment solution for Mac platform users, significantly simplifying the deployment process in Mac environments, and improving the deployment experience for developers and users.
山寒	Contributed Linux container deployment script for the EasyAIoT project, providing a containerized deployment solution for Linux platform users, achieving fast and reliable container deployment, and providing important guarantees for stable operation in production environments.
玖零。	Contributed Linux container deployment script for the EasyAIoT project, further improving the containerized deployment solution for Linux platforms, providing more options for users of different Linux distributions, and promoting the project's cross-platform deployment capabilities.

Special Thanks: The work of the above contributors has laid a solid foundation for EasyAIoT's cross-platform deployment capabilities. Their professionalism and selfless dedication are worthy of our learning and respect. Once again, we express our most sincere gratitude to these outstanding contributors! 🙏

## 🙏 Acknowledgements Thanks to the following contributors for code, feedback, donations, and support (in no particular order):

_大饼	_山寒	_{放学丶别走}	_春和	_{章鱼小丸子}	_Catwings	_小工头	_{西乡一粒沙}	_玖零。
_金鸿伟	_李江峰	_{Best Yao}	_无为而治	_shup	_也许	_{⁰ʚᦔrꫀꪖꪑ⁰ɞ .}	_逆	_廖东旺
_黄振	_春生	_{贵阳王老板}	_{hao_chen}	_尽千	_yuer629	_kong	_岁月静好	_Kunkka
_灬	_Mr.LuCkY	_泓	_i	_依依	_{小菜鸟先飞}	_追溯未来	_青衫	_Fae
_憨憨	_{文艺小青年}	_lion	_{汪汪队立大功}	_wcj	_{怒放de生命}	_蓝速传媒	_{Achieve_Xu}	_NicholasLD
_ADVISORYZ	_{take your time or}	_碎碎念.	_北街	_{Dorky TAT}	_右耳向西	_派大星	_棒槌	_{信微输传助手}
_一往无前	_Charon	_赵WIFI.	_Chao.	_{城市稻草人}	_{Bug写手墨白}	_kevin	_童年	_sherry金
_℡夏别	_翠翠草原	_慕容曦	_Tyrion	_大漠孤烟	_Return	_一杯拿铁	_Thuri	_Liu
_三金	_ZPort	_Li	_嘉树	_俊采星驰	_oi	_{ZhangY_000}	_℡夏别	_张瑞麟
_{Lion King}	_Frank	_徐梦阳	_九月	_tangl伟	_冯瑞伦	_杨林	_{梧桐有语。}	_{歌德de花烛}
_泥嚎	_翠翠草原	_胡泽龙	_苏叶	_裴先生	_谭远彪	_陈祺	_零点就睡	_风之羽
_王守仁	_狼图腾	_马到成功	_{做生活的高手}	_清欢之恋	_绝域时空	_风雨	_Nicola	_云住
_Mr.Zhang	_剑	_shen	_嗯	_周华	_太阳鸟	_了了	_{第七次日落}	_npc
_{承担不一样的天空}	_铁木	_Orion	_{森源-金福洪}	_薛继超	_虎虎虎	_Everyman	_NXL	_孙涛
_hrsjw1	_linguanghuan	_YiYaYiYaho	_慢慢慢	_lilOne

## 💡 Expectations

We welcome suggestions for improvement to help refine easyaiot.

## 📄 Copyright

Soaring Xiongkulu / easyaiot is licensed under the APACHE LICENSE 2.0. When using the product, please note the following:

Do not modify the source code header comments or attribution of the product-related code.
Do not use the product for any actions that endanger national security, honor, and interests, or for any illegal purposes.

## 🌟 Star Growth Trend Chart [![Stargazers over time](https://starchart.cc/soaring-xiongkulu/easyaiot.svg?variant=adaptive)](https://starchart.cc/soaring-xiongkulu/easyaiot)