Unified IoT Analytics Platform for Cyber-Physical Infrastructure
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Solution Overview
Problem
Current smart infrastructure platforms lack seamless integration of IT/OT/IoT applications, failing to provide actionable insights and prescriptive advisories due to interoperability issues, limited ability to handle unstructured data, and inability to address complex cross-system interactions and heterogeneity of information specifics, which hinders effective decision-making and operational optimization.
Innovation Solution
A unified context-aware IoT-based analytics platform that uses advanced Machine Learning models and cognitive intelligence to process diverse data sources, providing predictive and prescriptive analytics, automated collaborative workflows, and interactive dashboards for real-time situational response and optimization across cyber-physical infrastructure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a unified analytics system is implemented to aggregate information from multiple data sources, then interoperability and seamless resource sharing are improved, but device complexity and integration challenges increase
Solution Approach 1:
The patent implements an intermediary layer consisting of adapters, protocol translators, and data normalization services that mediate between diverse IT/OT/IoT systems and the unified analytics platform. This intermediary architecture enables seamless interoperability without requiring direct integration between all systems, thereby managing complexity while maintaining versatility.
Solution Approach 2:
The unified analytics system is designed with universal data ingestion capabilities that can handle multiple data formats, protocols, and source types through a common interface. The system provides multi-functional adapters and configurable connection templates that work across diverse infrastructure types, reducing integration complexity while maintaining broad adaptability.
2Measurement precision
If advanced intelligence models are used to uncover hidden patterns and trends, then analytical depth and insights are improved, but computational requirements and processing time increase
Solution Approach 1:
The analytics processing is segmented into multiple layers: data collection, preliminary processing, advanced analytics, and visualization. Each layer handles specific computational tasks independently, allowing advanced intelligence models to be applied selectively to subsets of data rather than processing entire datasets, thereby reducing overall computational power requirements while maintaining analytical precision.
Solution Approach 2:
The system performs preliminary data processing, filtering, and aggregation before applying advanced intelligence models. Data is pre-cleaned, normalized, and transformed into appropriate formats, which reduces the computational burden on downstream analytics engines while preserving the precision of pattern recognition and trend analysis.
3Speed
If real-time data processing is implemented for contextual insights, then response time and operational efficiency are improved, but system resource consumption and complexity increase
Solution Approach 1:
The system implements periodic batch processing combined with event-driven real-time processing. Routine analytics are performed at scheduled intervals using batch processing, while critical events trigger immediate real-time analysis. This hybrid approach provides timely responses for urgent matters while maintaining operational efficiency, without requiring continuous high-resource consumption that would increase system complexity.
4Loss of information
If comprehensive data aggregation from disparate systems is performed, then information completeness and analytical accuracy are improved, but data heterogeneity and processing difficulty increase
Solution Approach 1:
The system implements data normalization and standardization layers that transform heterogeneous data from diverse IT/OT/IoT sources into a unified schema. Data is converted to common formats, units, and structures, ensuring information completeness from all sources while reducing processing complexity through homogeneous data representation that simplifies downstream analytics.
Data Source
AI summary
The invention provides AI/Machine Learning frameworks i.e., a Workbench, (FIG. 2 (213)) with intelligences and modelling methods to address ‘What-If’ scenarios—(FIG. 2 (220)), users can devise specific studies (FIG. 3 (313)) with workbench to build and train models, ML workbench (FIG. 2 (213)) thus provides both domain-specific standard analytics as well as user-defined scenarios, while assisting the user to optimize their model performance, the seamless user Interface provides best-in-class visualization (FIG. 2 (226)) and dashboards (FIG. 2 (225)), while also enabling collaborative information specifics exchange (FIG. 9) and workflows across disciplines.