MUMBAI, India, Aug. 10 -- Intellectual Property India has published a patent application (202641090974 A) filed by Madhankumar C; Karthick B - Sns College Of Technology; Dr. S. V. Sudha - Rathinam Technical Campus; Ms R Rosalin Nancy - Rathinam Technical Campus; G. Ganesan - Vit Bhopal University; Mr. Rohit Kumar Singh - Dayananda Sagar University; and S. Kalpana - Jai Shriram Engineering College on July 27, 2026, for National Ai Digital Twin Platform For Real-Time Government Infrastructure Monitoring And Autonomous Public Asset Management.
Inventors include Karthick B - Sns College Of Technology; Dr. S. V. Sudha - Rathinam Technical Campus; Ms R Rosalin Nancy - Rathinam Technical Campus; G. Ganesan - Vit Bhopal University; Mr. Rohit Kumar Singh - Dayananda Sagar University; and S. Kalpana - Jai Shriram Engineering College.
The application for the patent was published on July 31, 2026, under issue no. 31/2026.
Abstract: National AI Digital Twin Platform for Real-Time Government Infrastructure Monitoring and Autonomous Public Asset Management Abstract The rapid expansion of smart governance initiatives has increased the need for intelligent platforms capable of monitoring, predicting, and autonomously managing critical public infrastructure. Conventional government asset management systems rely on fragmented databases, periodic inspections, and manual decision-making processes, which often result in delayed maintenance, inefficient resource utilization, and increased operational costs. To address these challenges, this work proposes a National AI Digital Twin Platform for Real Time Government Infrastructure Monitoring and Autonomous Public Asset Management, an integrated cyber-physical framework that combines Artificial Intelligence (AI), Digital Twin technology, Internet of Things (IoT), Geographic Information Systems (GIS), cloud-edge computing, and explainable analytics for intelligent governance. The proposed platform creates real-time digital replicas of government infrastructure assets, including roads, bridges, railways, airports, ports, public buildings, power grids, water distribution systems, healthcare facilities, educational institutions, and smart city infrastructure. Continuous data streams acquired from IoT sensors, drones, satellite imagery, surveillance systems, and environmental monitoring devices are synchronized with digital twins to provide a live representation of infrastructure health, operational status, environmental conditions, and asset utilization. Advanced AI models employing deep learning, computer vision, graph neural networks, and time-series forecasting analyze multimodal data to detect anomalies, predict structural degradation, estimate remaining useful life, and optimize maintenance schedules. A Large Language Model (LLM)-based autonomous governance engine provides intelligent reasoning by interpreting multimodal infrastructure data, government regulations, maintenance histories, and policy documents to generate explainable recommendations, automated maintenance plans, risk assessments, and decision support reports in natural language. Reinforcement learning continuously optimizes resource allocation, emergency response strategies, and infrastructure prioritization based on real-time operational requirements, budget constraints, and public service demands. Federated learning enables secure collaboration among multiple government departments while preserving data privacy and ensuring regulatory compliance. The platform incorporates predictive disaster management by integrating weather forecasts, seismic information, flood monitoring, traffic analytics, and environmental sensing to identify infrastructure vulnerabilities before failures occur. A secure blockchain-enabled audit mechanism ensures transparent asset tracking, maintenance verification, financial accountability, and tamper-resistant operational records. Interactive dashboards provide policymakers and administrators with real-time infrastructure health indices, digital twin visualizations, predictive maintenance alerts, asset utilization analytics, sustainability metrics, and AI-generated strategic recommendations. Experimental evaluation demonstrates that the proposed AI Digital Twin Platform significantly improves infrastructure monitoring accuracy, predictive maintenance efficiency, operational transparency, and public asset lifecycle management compared with conventional government infrastructure management systems. By integrating AI-driven analytics, Digital Twin technology, autonomous decision-making, and explainable intelligence within a unified national platform, the proposed framework enables proactive governance, optimizes public resource utilization, enhances infrastructure resilience, reduces maintenance costs, and supports the development of sustainable, data-driven, and citizen-centric smart government ecosystems.
Disclaimer: Curated by HT Syndication.
