| I&T Solution |
Development of Artificial Intelligence Lift and Escalator Automatic Inspection Planning System
(REF: S-1729) |
| Trial Project |
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| Solution Feature |
- AI powered software engine for lift & escalator maintenance planning and risk assessment.
- No GPU is needed for machine learning model training that saves hardware cost and energy consumption.
- User friendly dashboard interface.
- Edge computing, all computational processing are on the device itself. It allows for on-device data processing and enables real-time, context-aware decision-making.
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| Trial Application and Expected Outcome |
- A recommender engine that can accurately ranking the riskiest L/E based on the big data;
- Pipe line and transformation framework for conveying the updated big data to the recommender engine
- The engine shall able to refine the ranking according to the executed inspection result and various big data such as the incident database, issued warning letters and etc.
- The engine shall able to export the ranking to other system for further processing;
- 95% or above accuracy preferred.
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| Additional Solution Information |
IOE_solution.pdf
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| Info on I&T Solution Provider |
| Solution Provider | : | IOE Technologies Ltd | | Address | : | Unit 538B, 5/F, Core Building 2, HK Science Park, NT, Hong Kong | | Contact Person | : | Dr Jonathan Lee |
| Position | : | Director | | Tel | : | 37089681 | | Email | : |
ceo@ioetechnologies.net | | Webpage | : | www.ioetechnologies.net |
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