Industrial AI Data Aggregation for Baseline Deviation Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional enterprise systems face challenges in optimizing operations due to complex mathematical programming, dependency on intricate metadata models, and the overwhelming volume of data generated in industrial processes, which can lead to unachievable objectives and inefficient data processing.
Innovation Solution
The implementation of intelligent field-level devices (IFLDs) with on-board intelligence, capable of real-time adjustment and auto-configuration, to monitor and control equipment in industrial processes. These devices process data locally, reducing data transmission volume and providing context to the data, while also utilizing AI and historical data analysis to determine optimal configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional enterprise systems use complex mathematical programming and metadata models to optimize operations, then system optimization capability is improved, but device complexity and operational difficulty increase significantly
Solution Approach 1:
The system segments the complex enterprise optimization problem into multiple hierarchical levels (enterprise level, plant level, device level). Each level handles specific optimization tasks independently, with the device level performing local real-time optimization using simplified models, while higher levels handle strategic planning. This segmentation reduces the complexity burden on any single component while maintaining overall system optimization capability.
Solution Approach 2:
The patent introduces an intermediary layer (edge computing devices or gateway systems) between sensors and central control systems. This intermediary performs preliminary data processing, filtering, and aggregation, transforming raw sensor data into meaningful operational parameters. This reduces the complexity of data processing required at both the device level and enterprise level, while preserving optimization capabilities.
2Loss of information
If conventional systems process all sensor data through higher-level devices, then data processing capability is improved, but operational burden and processing time increase
Solution Approach 1:
The system performs preliminary data processing, filtering, and aggregation at the device level and edge level before transmitting data to central systems. Sensors and local controllers pre-process raw data to extract only relevant information, perform initial anomaly detection, and aggregate data over time periods. This preliminary action reduces the volume of data requiring central processing while ensuring no critical information is lost, thereby reducing processing time at higher levels.
Solution Approach 2:
The patent implements a multi-dimensional data processing architecture where data is processed simultaneously at multiple hierarchical levels (device level, edge level, enterprise level) rather than sequentially through a single central system. This dimensional change allows parallel processing of different data aspects at appropriate levels, significantly reducing overall processing time while maintaining comprehensive data analysis capability.
3Loss of information
If conventional systems transmit all generated data across the network, then data availability is improved, but data transmission volume and network burden increase
Solution Approach 1:
The system extracts and transmits only the most relevant and critical data elements from the vast sensor data stream. Local devices and edge systems filter out redundant information, transmit only anomalies and significant events, and send aggregated summaries rather than raw continuous data. This extraction approach maintains data availability for critical decisions while dramatically reducing network transmission volume and burden.
Solution Approach 2:
Different data processing and transmission strategies are applied locally at different hierarchical levels based on specific needs. Device-level systems process and transmit data with high granularity for local control, while edge systems aggregate and transmit summarized data for regional monitoring, and enterprise systems receive only strategic-level data. This local quality approach ensures data availability where needed while minimizing overall transmission volume through context-appropriate data handling at each location.
Data Source
AI summary
Various systems and methods are presented regarding monitoring and controlling operation of a process. A visual representation of the process can be created based on a supermodel comprising models (representing one or more devices) and nodes (representing respective device variables and constraints). Further, the process can be represented by levels, wherein devices at each level can be self-aware and have onboard artificial intelligence, such that a device at any level can auto-configure itself in accordance with a requirement placed upon it. Field-level devices (IFLDs) can be smart devices which auto-configure based upon a requirement from a higher-level device. Accordingly, system awareness can be incorporated across all levels of the process enabling overall and device-specific optimization of the process. IFLDs can auto-configure to collect and transmit data in accordance with an instruction from a higher-level device, leading to efficient data collection, reduced data bandwidth/processing, and expedited system optimization.