融合水网大模型与机理智能体:构建自主运行水网新范式OA

The development of national water networks has entered a stage characterized by refined regulation,moving beyond engineering connectivity and digital perception.Simultaneously,digital twin water conservancy continues to enhance its support for forecasting,early warning,scenario simulation,and contingency planning.However,the transition from decision support to autonomous decision-making and closed-loop control requires water network systems to address several challenges:meteorological and hydrological uncertainty,hydrodynamic nonlinearity,strong coupling among engineering components,difficulty in formalizing operating rules,and coordination across multiple operational objectives.To address these challenges,this paper proposed an autonomous water network operation architecture that integrated a water-network large model with mechanism-based agents.In this architecture,the water-network large model served as the cognitive decision-making hub,while mechanism-based agents acted as professional execution units for tasks such as flood control operation,water resources allocation,water conveyance and distribution control,ecological regulation,and water environment verification.These agents undertook hydrodynamic computation,optimal operation,control command generation,and constraint checking.The cognitive decision-making,professional execution,and feedback verification layers formed a closed-loop coordination through standardized interfaces and state feedback,linking business demand recognition,scheme generation,engineering control,and operational feedback.The paper further proposed an X-in-the-loop testing and verification system and a capability grading framework for autonomous operation,providing an architectural reference for the evolution of national water networks from decision support toward autonomous operation.