Distributed Analytics Nodes for Real-Time Industrial Data Processing
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Solution Overview
Problem
Industrial automation systems face challenges in efficiently analyzing and processing large volumes of data from industrial devices, leading to delays in decision-making and control responses due to centralized analytics systems that are not scalable or responsive to real-time data needs.
Innovation Solution
The implementation of a layered industrial analytics architecture with distributed analytic nodes across multiple layers of an industrial enterprise, allowing for real-time data processing and analysis at the most relevant layer, enabling immediate insights and decision-making through a scalable and modular architecture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If centralized analytics systems are used to process industrial data, then data processing capability is concentrated, but response time increases and scalability decreases
Solution Approach 1:
The patent segments the centralized analytics system into multiple distributed analytic nodes deployed across different layers (enterprise, system, device) of the industrial automation architecture. Each node independently processes data locally, eliminating the single-point bottleneck and reducing response latency while maintaining overall processing capability.
Solution Approach 2:
The patent introduces a spatial dimension to data processing by distributing analytic nodes across multiple hierarchical layers and physical locations. Instead of all data flowing through a single centralized point, processing occurs simultaneously at device level, system level, and enterprise level, creating a multi-dimensional processing architecture that reduces latency.
2Productivity
If centralized analytics systems are used, then data processing is consolidated, but system scalability is limited
Solution Approach 1:
The system is divided into independent, modular analytic nodes that can be individually added, removed, or upgraded at different hierarchical levels. This segmentation enables incremental scaling without requiring complete system redesign, allowing the architecture to adapt to growing data processing needs.
Solution Approach 2:
The analytic nodes are designed with universal interfaces and standardized data exchange protocols that allow them to function across multiple layers of the automation hierarchy. Each node can process various types of industrial data and communicate with different systems, providing versatility and ease of scaling.
3Measurement precision
If data is processed at centralized location, then analysis depth is sufficient, but real-time responsiveness is reduced
Solution Approach 1:
The patent implements local quality by enabling each analytic node to perform deep, context-specific analysis on data relevant to its layer. Device-level nodes conduct real-time sensor data analysis, system-level nodes perform intermediate processing, and enterprise-level nodes execute strategic analytics. This localized deep processing maintains analysis quality while enabling immediate responsiveness at each level.
Solution Approach 2:
The architecture performs preliminary data processing and filtering at device-level nodes before data is passed upward. This preliminary action reduces the data volume requiring deeper analysis at higher levels and enables immediate local responses without waiting for centralized processing, thus maintaining both analysis depth and real-time responsiveness.
4Loss of time
If distributed analytic nodes are deployed across multiple layers, then response time decreases and scalability improves, but system complexity increases
Solution Approach 1:
The patent implements dynamic adaptability where analytic nodes can automatically adjust their processing behavior based on data characteristics, layer requirements, and system state. Nodes dynamically allocate processing resources, adjust data transmission frequency, and coordinate with other nodes to optimize performance while managing complexity through adaptive rather than static configurations.
Solution Approach 2:
Standardized data exchange protocols and communication interfaces act as intermediaries between analytic nodes at different layers. These intermediaries simplify the complexity of inter-node communication by providing uniform data formats, authentication mechanisms, and error handling, allowing nodes to be added or modified without increasing overall system complexity.
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.