Book POC

Hydropower digital twin

Unplanned downtime and console workload map directly to generation revenue

为什么ROI极高

Holistic turbine sensing and predictive maintenance shorten fault handling and cut monitoring load — ROI shows up in downtime loss and ops manpower.

Loss unplanned downtime hours hourly generation value monitoring labor cost

Slow fault isolation — Complex unit conditions make root-cause tracing dependent on tribal knowledge and tool switching.
High console load — Alert floods bury critical signals and burn shift capacity.
Coarse maintenance plans — Without visual decision support, over- and under-maintenance coexist.

Start with holistic unit sensing and predictive maintenance, then close the loop from sensing → alert → work order → review.

Full-sensing turbines, predictive maintenance, and visual decisions that cut unplanned outages and inspection labor.

AI-desensitized; real results subject to offline experience

3D reconstruction of unit structure and operating conditions for faster localization.

AI-desensitized; real results subject to offline experience

Connect alerts, trends, and work-order history into a traceable chain.

AI-desensitized; real results subject to offline experience

Surface critical signals first to cut console overload.

AI-desensitized; real results subject to offline experience

Close the loop from alert to dispatch and review for measurable ops decisions.

↓40%

Fault handling time

↓60%

Monitoring workload

↑

Availability

Figures reflect public industry / landmark magnitudes for value density, not GINHE project commitments.

Start with holistic monitoring for one unit or hall to prove fault handling and console efficiency.

Typical POC: 6–12 weeks.