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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.

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.

• Multi-Protocol Camera Access Support: Comprehensive support for GB28181 and ONVIF, two mainstream video surveillance protocols, enabling standardized device access and management. GB28181, as China's national standard, perfectly adapts to mainstream domestic surveillance equipment; ONVIF, as an international universal standard, widely compatible with global mainstream camera brands. Through dual-protocol support, the platform seamlessly integrates with existing surveillance systems, achieving plug-and-play device access, automatic discovery, and unified management, significantly reducing device access barriers, enhancing system compatibility and scalability, and providing a solid technical foundation for large-scale camera deployment
• 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

• 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

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

EasyAIoT Platform Architecture

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.

EasyAIoT Platform Architecture

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.

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

Application Scenarios.png

EasyAIoT consists of five core projects:

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

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

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

EasyAIoT Platform Architecture

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.

• Platform Deployment Documentation

• Demo URL: http://36.111.47.113:8888/
• Username: admin
• Password: admin123

• Gitee: https://gitee.com/soaring-xiongkulu/easyaiot
• Github: https://github.com/soaring-xiongkulu/easyaiot

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:

知识星球

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:

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!

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! 🙏

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

We welcome suggestions for improvement to help refine easyaiot.

Soaring Xiongkulu / easyaiot is licensed under the MIT LICENSE. We are committed to promoting the popularization and development of AI technology, enabling more people to freely use and benefit from this technology.

Usage License: Individuals and enterprises can use it 100% free of charge, without the need to retain author or Copyright information. We believe the value of technology lies in its widespread use and continuous innovation, not in being bound by copyright. We hope you can freely use, modify, and distribute this project, making AI technology truly benefit everyone.

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