Submitted by admin on Fri, 08/19/2022 - 00:50

Inability to monitor all activities in a large area

Sequence Number 4 Industry Seaports Banner Inability to monitor all activities in a large area How 5G enabled

Wider coverage with AI’s ability to automatically detect authorised personnel and vehicles while monitoring activity, triggering alarms or alerts when necessary.

Data Flows
Title Devices Icon Devices Description
  • Sensors (cameras) located at multiple locations across the site: fixed/drones/robotics/cars/staff/etc.
  • Timeseries data to support camera images
Title Connectivity Icon Connectivity Description
  • Time series data transport
  • Camera images
  • Asset data (maintenance records) access
Title Edge Compute Icon Edge Compute Description
  • Several activities are in real-time
  • Camera – MV interpretation
Title Cloud Compute & Storage Icon Cloud Compute & Storage Description
  • All data collected from assets (both historical and real-time)
  • Enterprise-owned storage
Title Applications & Services Icon Applications & Services Description
  • Non-time critical activities
  • MV and some ML focus; E2E automated
  • Multiple MV models linked to apps
Title Inform Decision Makers Icon Inform Decision Makers Description
  • Errors and safety violations reported immediately to the operations centre of the site
Title Support Decision Making Icon Support Decision Making Description
  • End of process
Application Logic
Description
  • Camera-fitted drones would be a key enabler given the size of the area to be monitored.
  • 5G enables this implementation to be real-time where images are captured and sent to the edge storage and compute while flying.
  • Edge computing with MV will immediately analyse images and perform required actions, e.g., makes a direct call, steer drone to an area, trigger an alert, etc.
  • Edge + 5G to be used for all time critical events.
Description
  • All edge-collected data will be stored long-term in Enterprise storage.
  • Focus on MV to spot the data needed for the different scenarios.
  • Development of the ML model is done through an iterative process. A quality ML model (fully data-driven) will require multiple steps to detect anomalies and potential failures.
  • Models will be stored and maintained by AI applications.
  • SMEs and data scientists to develop these models.
Description
  • The process from data collection to execution of models is fully automated.
  • MV models developed to support all application cases; MV model needed for each application case.
  • Various options for visualisation of entire operations (e.g., XR options on OpenXR platform).
Expected benefits Key value created