低功耗水下监测系统实现本地智能推理，节省能源并减少数据传输。
Energy Constrained Hierarchical Underwater Monitoring via Local Multi-Agent RAG
- 边缘设备持续感知，仅在需要时唤醒高性能计算单元进行本地多模态分析。
- 通过嵌入索引与检索增强推理，实现物种识别准确率提升，数据压缩比达85%。
- 适合长期部署的海洋生态监测项目，尤其适合资源受限的野外研究团队。
海洋生物监测受限于严苛的能耗约束、水下通信质量差以及远程部署中原始多模态数据传输成本高昂。本文提出一种低功耗水下监测架构，结合持续运行的边缘传感与选择性高性能本地推理。系统采用分层主-卫星设计：超低功耗MAX78000/MAX78002微控制器持续监控视觉与声学信号，而NVIDIA Jetson Orin NX仅在预定处理、事件触发或研究人员交互时激活。就绪后，Jetson执行完全本地化的多模态流程：数据接入、目标提取、基于嵌入的索引、物种识别、检索增强推理及自动生成报告。使用BioCLIP/OpenCLIP嵌入将任务数据、海洋分类参考文献、科学文档与操作元数据组织至本地ChromaDB库。专用识别层融合视觉相似性搜索、质心分类与监督分类器，支持自适应物种识别。基于LangChain的多智能体框架协调查询路由、结构化分析、能耗管理、硬件重构与报告生成。通过视觉与声学监测案例评估该系统。所提架构将超低功耗持续感知与本地多模态智能结合，使水下节点可生成结构化、研究可用的知识，同时对本地数据压缩85%，支持灵活的声学、光学或卫星传输，显著降低能耗与通信开销。
原文摘要 · Abstract (English)
Marine life monitoring is limited by strict energy constraints, poor underwater connectivity, and the high cost of transmitting raw multimodal data from remote deployments. This paper proposes a low-consumption underwater monitoring architecture that combines always-on edge sensing with selective high-performance local reasoning. The system follows a hierarchical master--satellite design in which ultra-low-power MAX78000/MAX78002 microcontrollers continuously monitor visual and acoustic signals, while an NVIDIA Jetson Orin NX is activated only for scheduled processing, event-driven analysis, or researcher interaction. Once active, the Jetson executes a fully local multimodal pipeline for data ingestion, visual target extraction, embedding-based indexing, species identification, retrieval-augmented reasoning, and automated reporting. BioCLIP/OpenCLIP embeddings are used to organize mission data, marine taxonomic references, scientific documents, and operational metadata in local ChromaDB collections. A dedicated identification layer combines visual similarity search, centroid-based classification, and supervised classifiers to support adaptive species recognition. A LangChain-based multi-agent framework coordinates query routing, structured analysis, energy management, hardware reconfiguration, and report generation. The architecture is evaluated through visual and acoustic monitoring case studies. The proposed system bridges ultra-low-power continuous sensing with local multimodal intelligence, enabling underwater stations to produce structured, researcher-ready knowledge while compressing local data for flexible acoustic, optical, or satellite transmission, minimizing both energy use and communication overhead.
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