Distributed Analytics Nodes for Industrial Automation Latency
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Solution Overview
Problem
Industrial automation systems face challenges in efficiently analyzing and processing large volumes of data across multiple levels of an industrial enterprise, leading to delayed decision-making and reduced system responsiveness due to centralized analytics approaches.
Innovation Solution
A scalable analytics system is implemented using distributed analytic nodes across multiple levels of an industrial enterprise, enabling collaborative analytics operations and data exchange between nodes to process data at the most relevant level, reducing latency and enhancing responsiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If centralized analytics approaches are used to process industrial data, then data processing completeness is improved, but system responsiveness deteriorates due to delayed decision-making
Solution Approach 1:
The patent segments the centralized analytics system into multiple distributed analytic nodes deployed across different levels of the industrial enterprise (enterprise level, system level, device level). Each node independently processes data relevant to its level, eliminating the single-point bottleneck of centralized processing while maintaining comprehensive data analysis through hierarchical collaboration.
Solution Approach 2:
The patent introduces a hierarchical dimension to the analytics architecture, organizing analytic nodes across multiple levels (enterprise, system, device) rather than a single centralized point. This dimensional transformation enables parallel processing at different hierarchical levels, simultaneously achieving comprehensive data processing and rapid local decision-making.
2Manufacturing precision
If centralized data processing is implemented, then data analysis thoroughness is improved, but device complexity increases due to centralized infrastructure requirements
Solution Approach 1:
The patent divides the centralized analytics infrastructure into multiple independent analytic nodes distributed across the enterprise hierarchy. Each node contains essential analytics components (data collection, processing, visualization) and operates semi-autonomously, reducing the complexity burden on any single system while collectively achieving thorough data analysis.
Solution Approach 2:
The patent enables each analytic node to be tailored to its specific hierarchical level (enterprise, system, or device level) with customized analytics capabilities and data processing requirements. This local optimization allows each node to have appropriate complexity for its function, rather than requiring all nodes to match the complexity of a centralized system.
3Loss of time
If distributed analytic nodes are deployed across multiple levels, then system responsiveness is improved through real-time local processing, but device complexity increases due to multi-node deployment
Solution Approach 1:
The patent merges multiple distributed analytic nodes into a unified hierarchical architecture where nodes at different levels (enterprise, system, device) work together through standardized interfaces and protocols. This consolidation provides centralized coordination benefits while maintaining local processing responsiveness, effectively managing the complexity of multi-node deployment through architectural integration.
4Productivity
If analytics operations are distributed across hierarchical levels, then productivity is improved through parallel processing, but loss of information increases due to data distribution across nodes
Solution Approach 1:
The patent implements feedback mechanisms where analytic nodes at different hierarchical levels exchange data and results through standardized interfaces. Lower-level nodes (device, system) provide processed data to higher-level nodes (enterprise), which can request additional information or validation, ensuring data consistency and preventing information loss through continuous verification and communication loops.
Data Source
AI summary
A layered industrial analytics architecture enables the flow of information from intelligent assets into tools and engines that perform analytics and enable decision-making in substantially real-time. The analytics architecture comprises analytic nodes that are distributed across multiple layers of an industrial enterprise, and includes system features that optimize movement of data across this layered architecture. Each analytic node includes base architectural constructs that host various analytic, data acquisition, and storage elements. These base constructs can operate autonomously, or in conjunction with other instances of base constructs or other elements of the control system. The system design uses a multi-platform compatible implementation that allows the base elements to be deployed on various different computing platforms.