Summary
AI + Deep Application leads the new direction of water utility development. Leveraging its enterprise service large model inGPT, Inspur integrates core business models such as pipe burst analysis and identification, water balance analysis, and chemical dosing analysis to enhance precise perception, intelligent identification, and proactive warning capabilities at the operational decision-making level.
Background
Water utility companies have accumulated large volumes of operational data, but most of it is used only for report statistics. Decisions on chemical dosing, pressure control, leak management, and emergency response still rely on experience, and problems are often discovered only after they occur. Converting data into models and then models into applications is a key step for water utilities to move from informatization to intelligence.
Solution Description
1. Industry Models and Application Models
Building on the inGPT general model and industry models, application models such as chemical dosing analysis models, water balance analysis models, safety production identification models, equipment anomaly analysis models, pipe burst analysis models, and natural language dialogue models are constructed.
2. Supporting Platform
The application models are supported by the AIoT foundation, big data middle platform, and enterprise digital capability platform, providing real-time data, data governance, and model training and inference service capabilities.
3. Application Scenarios
The models are deployed in scenarios such as smart water plants, automatic control, intelligent customer service, intelligent scheduling, intelligent security, automatic form submission, automatic proofreading, automatic document processing, and personalized advancement, forming an intelligent application system covering the entire water utility business.
Customer Value
Chemical dosing and process control become more precise, reducing chemical consumption and energy consumption, and making effluent water quality more stable. Pipe bursts and equipment anomalies are identified earlier and handled faster, reducing leakage, non-revenue water, and water supply interruptions. Repetitive copywriting and form work is automated, and operators shift from passive response to proactive, model-based decision-making.
