NIVUS presented sensor-based AI system for emergency services, municipalities, citizens and industry at IFAT
With FloodLead, the company from Eppingen offers a digital service for early alerting and monitoring of heavy rain and floods. Users benefit from a valuable gain in time for precaution and damage limitation.
In the event of heavy rainfall, it is essential for emergency services to optimise alarm and deployment plans and to organise operations as effectively as possible. Likewise, citizens in affected situations want to be informed in time to take protective measures for material assets or their own health. If they are informed in good time, industrial companies can protect their plants and stocks and avoid unplanned production downtimes.
The easy-to-understand information service alerts in time and reliably to threatening dangers from heavy rain, sewer network overload and river flooding. Core elements of the system are a self-learning intelligence and a network of swarm sensors. In combination with public weather data, NIVUS offer spatial and temporal resolution of the alarms in a new quality. Existing sensors such as rain gauges, level measurement systems or level measurements are integrated into the monitoring system. If there are no existing sensors or in the case of gaps, the provider fills these with energy-autonomous and smart sensor technology.
For fail-safe operation, NIVUS provide the service as a full service package. Municipalities and associations thus have only one responsible contact person and can focus their own resources entirely on the organisation of the measures.
One of the FloodLead technologies is detection and forecast of heavy rainfall cells from IoT precipitation measurements. This was developed by the technology partner Okeanos Smart Data Solutions GmbH. The development was funded by the BMVI (German Federal Ministry for Digital Affairs and Transport). FloodLead is currently being applied in pilot areas. NIVUS offer special conditions for project partners for further pilot projects.
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