Industrial Digital Twin UI With Reduced-Dimensional Role Views
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Solution Overview
Problem
Industrial environments face challenges in effectively utilizing vast amounts of data from IoT sensors, such as vibration data, to improve operations and maintenance, due to complexity and the need for role-specific insights that current digital twin technologies cannot adequately provide.
Innovation Solution
An enterprise management platform with role-specific executive digital twins that integrate AI-enabled features and enhanced collaboration, enabling executives to monitor and control industrial plant operations through a converged technology stack for intelligent sensing, data collection, and real-time data handling, and deployment of AI capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If vast amounts of data from IoT sensors are collected and analyzed, then operational insights and maintenance improvements are enhanced, but system complexity and data processing requirements increase
Solution Approach 1:
The patent segments the industrial system into multiple digital twins representing different levels of the organization (executive, plant manager, operations supervisor, operations worker). Each digital twin processes and presents data at its specific level, dividing the complex data processing task into manageable segments that address different operational needs without requiring all users to handle the full complexity of the entire data set.
Solution Approach 2:
The patent adds an organizational hierarchy dimension to the data processing architecture. Instead of a flat data processing system, it creates multiple layers corresponding to different organizational levels, where each layer processes data relevant to its specific decision-making needs. This dimensional addition allows the system to manage complexity by organizing data processing along the hierarchical axis.
2Ease of operation
If role-specific insights are provided to different organizational levels, then decision-making effectiveness is improved, but information customization and processing requirements increase
Solution Approach 1:
The patent applies local quality by providing different types of information to different organizational levels. Each digital twin is configured with specific data and visualization appropriate to its level: executive digital twins receive high-level operational status and KPIs, while operations worker digital twins receive detailed equipment data and maintenance instructions. This ensures each user receives information with the appropriate level of detail and relevance to their specific role.
Solution Approach 2:
The digital twin architecture serves multiple functions across different organizational levels simultaneously. A single digital twin system provides executive oversight, plant-level management, operational supervision, and equipment-level monitoring through its hierarchical structure. This multi-functionality allows the system to address diverse information needs without requiring separate systems for each organizational level.
3Productivity
If real-time data handling and AI capabilities are deployed, then operational control and monitoring are enhanced, but computational requirements and system resources increase
Solution Approach 1:
The patent implements preliminary action by pre-configuring digital twins with their specific data requirements and processing capabilities before deployment. Each digital twin is prepared with the appropriate AI models, data filters, and visualization templates needed for its specific organizational level. This preliminary setup allows the system to efficiently process real-time data without requiring excessive computational resources during operation, as the processing framework is already in place.
Data Source
AI summary
Methods generally including determining a structure in the data; by the controller, determining a relevance of the determined structure in the data to at least one role type stored within a role taxonomy; by the controller, determining a reduced dimensionality view of the data in response to the determined structure in the data. The reduced dimensionality view comprises fewer dimensions than the data from the plurality of input sensors. The reduced dimensionality view further comprises a graphical element representing at least one of: mechanical portions of a machine of the industrial environment, or a sensor from the plurality of input sensors that provided data; and providing the reduced dimensionality view to a user interface that is associated with at least one entity associated with the at least one role type stored within the role taxonomy.