Industrial AI Model Hierarchy for Real-Time Process Auto-Configuration
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 across automated processes, which can lead to unachievable objectives and operational burdens.
Innovation Solution
A system comprising a processor and memory that executes computer-executable components to construct graphical representations of industrial processes. This system includes configuration and visualization components to model and present the process, detect changes in device operating conditions, and update representations accordingly, enabling real-time monitoring and control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional enterprise systems use complex mathematical programming and intricate metadata models to optimize operations, then the system can handle enterprise-wide optimization requirements, but the system complexity increases and becomes difficult to manage and explain
Solution Approach 1:
The patent segments the enterprise system into multiple hierarchical levels (enterprise level, plant level, area level, device level) with each level having its own simplified model. This segmentation allows complex enterprise-wide optimization to be achieved through coordinated simpler models at each level, rather than requiring a single monolithic complex model.
Solution Approach 2:
The patent introduces intermediate controllers (PLCs, area controllers) that act as mediators between field devices and higher-level enterprise systems. These intermediaries process and simplify data locally, reducing the complexity burden on both the field devices and the enterprise-level optimization system.
2Loss of information
If conventional systems process large volumes of sensor data through higher-level devices, then complete data processing can be achieved, but the operational burden on higher-level devices increases significantly
Solution Approach 1:
The patent segments data processing responsibilities across multiple hierarchical levels. Field devices perform local processing and filtering, intermediate controllers perform area-level processing, and only essential processed data is transmitted to enterprise-level systems. This segmentation distributes the operational burden and prevents data overload at any single level.
Solution Approach 2:
The patent implements preliminary data processing and filtering at lower hierarchical levels before data reaches higher-level devices. Field devices and intermediate controllers pre-process sensor data, eliminating unnecessary information and reducing the data volume that higher-level devices must handle, thereby reducing their operational burden.
3Loss of information
If conventional systems transmit all generated sensor data across the automated process, then complete operational data is available, but the data volume becomes unwieldy and difficult to manage
Solution Approach 1:
The patent extracts and transmits only the essential and relevant data elements through each hierarchical level, rather than transmitting all raw sensor data. Field devices extract key parameters, intermediate controllers extract area-level summaries, and only critical processed data is transmitted upward, significantly reducing data volume while maintaining necessary information completeness.
Solution Approach 2:
The patent segments data transmission into hierarchical layers with different data volumes and granularities. Each level transmits data appropriate to its function, with field devices transmitting device-level data, intermediate controllers transmitting area-level aggregated data, and enterprise systems receiving only strategic-level summaries, thereby managing data volume effectively.
4Adaptability or versatility
If conventional systems use highly dependent complex metadata models, then the system can meet enterprise objectives, but the models become non-explainable and difficult to interpret
Solution Approach 1:
The patent segments the complex enterprise-wide optimization problem into simpler sub-problems at each hierarchical level, each with its own explainable model. The enterprise objective is achieved through coordinated optimization at multiple levels, with each level's model being simpler and more explainable than a single monolithic model would be, while collectively achieving the same overall objective.
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.