Case study · Smart City
The city’s lifelines,watched in one place.
A major metropolitan government overseeing a rapidly growing city of more than 9 million residents. The city was among the first in its country to build an integrated control platform for its whole urban infrastructure, from gas and water supply to power networks and bridges. After severe regional flooding displaced hundreds of thousands of residents, the city concluded that while safety systems cannot prevent a natural disaster, they can stop the cascade that follows it: power cuts, interrupted water, communications blackouts, each one as much of an interruption as the event itself.
Constraints and answers
The disaster is rarely the problem. The cascade after it is.
One center, and the thirty models behind it
Every lifeline managed in its own silo
Gas, water, drainage, heat, bridges, elevators and river environments were each managed in a disconnected silo, with no unified monitoring.
Leakage and corrosion go undetected underground
Underground pipelines are highly susceptible to undetected leakage, corrosion and gas accumulation, with potential for a chain event: leakage, explosion, collapse.
Response began only after the incident
There was no predictive risk analysis; the city relied on reactive emergency response after an incident had happened.
No real-time fusion of multi-source data
Flood, water pollution and structural degradation across bridges and pipelines required real-time multi-source data fusion at massive scale.
No models for the city’s specific risks
There were no AI models for tracing pollution sources, predicting flooding, assessing bridge health or simulating gas dispersion.
Urban lifelines safety monitoring center
IoT sensors, cloud computing, big data and AI in one platform, monitoring every piece of critical urban infrastructure in real time.
Thirty AI analysis models our team built
Covering the full spectrum of urban safety risk: pipeline diagnosis, water leak detection, gas identification and dispersion, bridge health, flood warning and pollution tracing.
Laser-based intelligent gas monitor
TDLAS laser technology for the gas pipeline network and the underground infrastructure around it, carrying major equipment manufacturing certification and national recognition for scientific progress.
Risk analysis and event-chain modeling
Real-time IoT monitoring combined with external data for daily hazard identification, risk analysis, forecasting, early warning, diffusion analysis and event-chain modeling.
Hazard identification is a daily job now, not an emergency one.
The results
2,200 km of pipeline watched, accidents down 60%.
- 2,200+ km
- PIPELINE MONITORING COVERAGE
- 822 km gas, 739 km water, 254 km drainage, 201 km heat, 58.5 km pipe gallery, plus 150 km of river and 51 bridges.
- 60%
- FEWER PIPE NETWORK ACCIDENTS
- Hazard screening 70% more efficient, with over 6,000 emergencies forecast and warned since implementation.
- 50bn
- DATA ENTRIES PROCESSED DAILY
- Real-time multi-source fusion across sensors, cameras and monitoring systems city-wide.
- 30
- AI MODELS IN SERVICE
- Pipeline monitoring, flood prediction, bridge health, gas dispersion and pollution tracing.
Fifty billion readings a day only matter because thirty models are reading them.
The stack
What was deployed
Wireless sensors, HD cameras, RFID tags, edge devices and mobile terminals across more than 2,200 km of urban infrastructure.
Data aggregation, governance and services, processing 50 billion entries a day for city-wide monitoring.
Speech, deep learning, visual computing, NLP and machine learning engines behind the thirty urban safety models.
Elastic CPU, distributed storage, GPU and FPGA compute, carrying real-time risk analysis and multi-layer early warning.
Примечание о возможностях
Возможности, компетенции и примеры внедрений, представленные на этом сайте, отражают совместную работу ключевой инженерной команды Amanah. Некоторые проекты были реализованы через предыдущие или аффилированные организации. Суверенные внедрения конфиденциальны по мандату, поэтому отдельные клиенты не называются. Соответствующие референсы подлежат проверке в рамках NDA.
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