How many of our problems can this system actually solve? What are the cost-reduction and efficiency gains? How much labor does it replace?

1. What specific problems does the system solve?

• Solves blind spots: areas traditionally hard to reach such as bridge bottoms, box girders and bearings achieve automatic inspection with no manual-operation blind spots.
• Improves efficiency: a 500m-class bridge generates nearly 100GB of data and over ten thousand photos per inspection, automatically screened and detected by AI, replacing manual one-by-one review; modeling is completed in hours and reused thereafter.
• Data value: defects are pixel-mapped onto the 3D model, multi-period comparison observes development, and historical data no longer sleeps in paper reports.
• Periodic inspection cost reduction: once routine and periodic inspection data are connected, periodic inspection no longer requires a full-bridge scan; reports are generated from fixed-point verification alone, greatly reducing labor and time costs.

2. How are cost reduction and efficiency gains reflected?

The specific number of labor positions replaced and the scale of cost reduction vary by project scale and inspection frequency; it is recommended to calculate based on the actual number of bridges and frequency. The core logic is: hand repetitive manual tasks (photographing, image review, archiving) to automation and AI, with human effort focused on review and decisions.

3. Applicable scenarios for system value

4. Applicable conditions and scenarios

• Owners with many bridges and high inspection frequency
• Maintenance agencies focused on labor cost and operational safety
• Specific benefits calculated by project plan

5. Notes

Benefit calculation must combine actual project data; it is recommended to provide the bridge list and inspection frequency during the solution phase for quantitative evaluation.