Plant Causal Analysis Interface for Faster Operator Decisions
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Solution Overview
Problem
Operators face difficulties in making quick decisions due to the complexity of causal relationships displayed in process data from plants, especially for those with limited experience, as existing methods struggle to simplify and present actionable information effectively.
Innovation Solution
An analysis method and device that utilize a causal model, specifically a Bayesian network, to acquire prediction results, identify relevant variables, and display their states and statistics, assisting operators in understanding and acting upon plant data effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If causal relationships are displayed to enable immediate operation, then experienced operators can quickly specify measures, but operators with limited experience become confused due to data complexity
Solution Approach 1:
The patent segments the complex causal relationship data into multiple hierarchical levels. The display system divides the full causal model into sub-causal relationships or partial views that are presented to operators based on their experience level and the specific operational context. This segmentation reduces the perceived complexity while preserving the complete causal information structure for when it is needed.
Solution Approach 2:
The patent implements dynamic adaptability in the display system that adjusts the level of detail and complexity of causal relationship information based on operator characteristics, situational context, and interaction history. The system dynamically modifies what causal data is presented, transforming from a static complex display to a dynamic adaptive interface that optimizes for each user's needs.
2Loss of information
If complex multidimensional process data is analyzed to specify causal relationships, then complete causal information is obtained, but operators with limited experience find it difficult to take immediate action
Solution Approach 1:
The patent performs preliminary organization and structuring of causal relationship data before presentation to operators. The system pre-processes the complex multidimensional process data into organized causal models, pre-calculates relevant statistics, and prepares multiple levels of abstraction in advance. This preliminary action ensures that when operators need information, it is already structured and ready for rapid consumption without requiring them to process raw complex data.
Solution Approach 2:
The patent introduces an intelligent intermediary system (the analysis device) that acts as a mediator between the complex process data and the operator. This intermediary automatically analyzes the multidimensional data, identifies causal relationships, and presents them in operator-friendly formats. The intermediary handles the complexity burden, allowing operators to make quick decisions without directly grappling with the full complexity of the underlying data.
3Ease of operation
If data reduction is performed to simplify information display, then ease of understanding improves, but experienced operators lose the ability to quickly specify measures
Solution Approach 1:
The patent implements a nested information structure where simplified causal relationship views are contained within the broader context of the complete causal model. Operators can access simplified representations (the inner doll) for quick understanding, while the ability to expand and access the full detailed causal relationships (the outer dolls) remains available. This nesting preserves both the simplified view for ease of understanding and the complete information structure for experienced operators who need detailed analysis.
Data Source
AI summary
An information processing device acquires a prediction result obtained when a premise is applied to a causal model having a plurality of variables related to operation of a plant. Based on the prediction result, the information processing device specifies a relevant variable dependent on the premise from the plurality of variables. Thereafter, with respect to the relevant variable, the information processing device displays information on a state of the relevant variable obtained according to the prediction result and a statistic of plant data corresponding to the relevant variable in plant data that is generated in the plant.