A Hydro-Intelligence Body: Cognitive Architecture for Project-Oriented Autonomous Water Systems Driven by Multi-Modal Sensing and Water Utility Panel

The advancement of emerging technologies such as unmanned aerial vehicles and distributed sensing devices has accelerated the development of Autonomous Intelligent Systems (AIS) with integrated perception, communication, and computation capabilities. However, in mission-critical consumer applications such as smart cities, a major challenge lies in designing a computational architecture that combines deployment flexibility with domain-specific expertise, enabling continuous self-evolution and collaborative decision-making based on dynamic sensing data. To address this challenge, we propose a novel hierarchical cognitive architecture. The framework adopts a project-based methodology, where project engineers can autonomously construct systems by selecting utilities from a utility panel that integrates physical models, data-driven models, and AI models. The system’s intelligence is powered by federated knowledge engine, which coordinates a set of domain-specific fine-tuned large language models (LLMs), and orchestrates their collaboration through a multi-agent workflow to execute complex tasks. We conduct extensive experiments demonstrating that, compared with several general-purpose LLMs, our fine-tuned model under our approach exhibits a significantly lower frequency of moderate-to-severe hallucinations in the water resources engineering domain. These findings validate the feasibility of constructing a federated knowledge engine. Furthermore, this work provides a robust and scalable platform for developing next-generation consumer-grade AIS that require high reliability and physical interpretability.