Industrial AI Configuration Parsing for Hierarchical Process Control
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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 across automated processes, which can lead to unachievable objectives and operational burdens on higher-level devices.
Innovation Solution
The system employs a device with on-board intelligence to monitor and control equipment in a process, using an AI component to generate models replicating device operations and a configuration component to incorporate these models into a graphical representation of the industrial process, enabling real-time adjustments and data processing at the field level.
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 burden increase significantly
Solution Approach 1:
The patent segments the enterprise system into multiple hierarchical levels (enterprise level, site level, area level, device level), with each level handling specific optimization tasks. This segmentation reduces the complexity at any single level by distributing computational burdens across the hierarchy, allowing local devices to make decisions without requiring complex enterprise-wide models.
Solution Approach 2:
The patent introduces intermediate controllers (PLCs, distributed control systems) that act as mediators between field devices and higher-level enterprise systems. These intermediaries process and filter data locally, reducing the complexity of direct communication and model management between individual devices and the enterprise system.
2Productivity
If conventional systems process large volumes of sensor data through higher-level devices, then data processing capability is improved, but operational burden on higher-level devices increases
Solution Approach 1:
The patent divides data processing responsibilities across multiple hierarchical levels. Field-level devices perform basic data collection and filtering, area-level controllers aggregate and process data from multiple devices, and enterprise-level systems handle strategic analysis. This segmentation reduces the operational burden on any single level while maintaining overall data processing capability.
Solution Approach 2:
The patent implements preliminary data processing and filtering at lower hierarchical levels before data reaches higher-level devices. Field devices and area controllers pre-process sensor data, filtering out noise and aggregating relevant information, which reduces the volume and complexity of data that higher-level devices must process.
3Measurement precision
If conventional enterprise systems use highly complex metadata models, then system modeling accuracy is improved, but model explainability and usability decrease
Solution Approach 1:
The patent segments complex enterprise-wide models into smaller, localized models at different hierarchical levels. Each level maintains models appropriate to its scope (device-level models, area-level models, enterprise-level models), which are simpler and more explainable than a single comprehensive enterprise model, while collectively achieving high modeling accuracy.
Solution Approach 2:
The patent applies different levels of modeling detail and complexity appropriate to each hierarchical level and local context. Local devices use simple models for immediate control, while higher levels use more sophisticated models for strategic optimization. This local quality approach maintains explainability at each level while achieving overall system accuracy.
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.