助力维护工程安全、供水安全、水质安全 南水北调“天河”大模型正式发布

Core Insights - The South-to-North Water Diversion "Tianhe" model has been officially released, leveraging artificial intelligence to enhance the smart construction and operation of the national water network, ensuring the safety of the South-to-North Water Diversion project, water supply, and water quality [1][2]. Group 1: Model Development - The "Tianhe" model establishes a smart computing cloud platform, creating two main platforms: AI middle platform and data middle platform, along with a matrix of three major model systems: water network large language model, water network visual model, and hydraulic professional model [1]. - The model is capable of understanding, simulating, predicting, and assisting in decision-making for complex water system behaviors, transitioning from human-driven experience to intelligent perception and precise early warning [1]. Group 2: Operational Management - In terms of operation and maintenance, the "Tianhe" model constructs a multi-agent collaborative system for simulation, scheduling, control, and maintenance, relying on a digital twin water network for high-precision simulation and autonomous decision-making [1]. - A "sky-ground" perception network composed of drones and robotic dogs achieves automatic identification and early warning of hidden faults, with an accuracy rate of over 98% for key part identification [1]. Group 3: Safety Supervision - The model integrates multi-source data from meteorology, hydrology, engineering operations, and construction, enabling rolling forecasts for the next 15 days and intelligent simulations of flood processes, enhancing the ability to predict flood disasters and the scientific nature of emergency decision-making [2]. - For major engineering projects like the Jiang River supplementary project, the related intelligent agents will effectively improve comprehensive forecasting efficiency and construction collaboration, thereby reducing construction risks [2]. Group 4: Water Quality Protection - The model allows for second-level retrieval of water quality knowledge and minute-level simulations of pollution diffusion, significantly compressing algal identification time from hours to seconds, thereby strengthening the water quality safety defense [2].