projOPENVELA-MCP

# Design of a Multimodal Edge Intelligence Collaborative Framework Based on openvela and MCP - 2025 Functional Track Challenge ## **Requirements** ### **Functional Requirements** 1. **Multimodal Perception**: Implement the collection and preliminary processing of various input modalities (such as visual, auditory, and sensor data) on openvela. 2. **MCP (Model Context Protocol) Implementation**: Develop a lightweight MCP Server on resource-constrained openvela, abstracting device capabilities into Resources and Tools. 3. **Intelligent Collaboration**: Build the collaborative logic between the MCP Client and large models, enabling the large model to make inference decisions based on multimodal data from edge devices. 4. **Cross-Device Control**: Enable the large model to remotely invoke the capabilities of edge devices through the MCP protocol to execute actual control tasks. ### **Performance Requirements** On hardware such as STM32/ESP32 P4/Raspberry Pi: 1. Multimodal data processing response time ≤ 500ms. 2. MCP communication latency ≤ 200ms. 3. System stability (hardware response success rate ≥ 95%). ### **Application Scenarios** - Smart home environmental perception and control. - Intelligent security anomaly detection. - Collaborative robot multimodal interaction in AIoT scenarios. ### **Features** - Lightweight Design: Optimize the MCP Server implementation for the resource-constrained characteristics of openvela. - Multimodal Fusion: Design fusion strategies and prioritization mechanisms for different modality data. - Standardized Interface: Implement standardized interactions between edge devices and large models based on the MCP protocol. - Adaptive Collaboration: The large model can adaptively invoke appropriate device capabilities based on the multimodal context from the edge. ### **Expected Goals** 1. Complete the multimodal data collection and processing module based on openvela. 2. Implement a lightweight Server compliant with MCP specifications that can expose multimodal Resources and Tools of devices. 3. Develop an MCP Client integrated with large models to realize intelligent collaborative logic. 4. Construct and validate a complete AIoT application scenario to demonstrate multimodal intelligent collaboration capabilities. 5. Submit design documentation (including system architecture diagrams, communication flow diagrams), source code, and demonstration videos. 6. License: Apache License, Version 2.0 ## **Mentor Information** **Zhou Wenjie** **zhouwenjie1@xiaomi.com** ## **Difficulty** High (requires integration of multimodal interaction algorithms, edge computing, large model collaboration, and other technologies) ## **Category** AI Applications ## **References** 1. openvela [official documentation](https://github.com/open-vela/docs) and [open-source code](https://github.com/open-vela) 2. MCP official protocol: https://github.com/modelcontextprotocol 3. Vela JS application development documentation: https://iot.mi.com/vela/quickapp/zh/guide/ ## **Notes** ### **Recommended Tech Stack** - Edge Side: openvela, lightweight machine learning frameworks (such as TensorFlow Lite) - Communication Layer: Lightweight MCP Server protocol implementation based on TCP/IP - Large Model Side: Function Calling or Agent framework integrated with MCP Client ### **Expansion Directions** - Increase lightweight model inference capabilities on the edge side to reduce communication burden. - Design a context memory mechanism for large models and edge devices to enhance interaction intelligence. - Explore Privacy-Preserving multimodal data processing solutions. ### **Scoring Focus** - Innovation in multimodal data processing and fusion. - Optimization strategies for MCP implementation in resource-constrained environments. - Intelligence level of collaboration logic between large models and edge devices. - Overall reliability and practicality of the system. ### **Reference Architecture Diagram**