Industrial Configuration Recommendations Using Graph Neural Networks
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Solution Overview
Problem
Conventional recommendation systems for configuring industrial systems fail to provide information on how to connect configured components and set configurable attributes, relying on predefined rules or collaborative filtering without encoding component features and topology effectively.
Innovation Solution
A computer-implemented method using a trained graph neural network to encode component features and topology, generating recommendations for connecting components and setting attributes by calculating scores based on context-aware embeddings and matrix multiplications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional recommendation systems use predefined rules or collaborative filtering, then implementation is simple, but they fail to provide information on how to connect components and set attributes
Solution Approach 1:
The patent replaces conventional recommendation systems (based on predefined rules or collaborative filtering) with a graph neural network system. This substitution enables the system to encode component features and topology, providing comprehensive information about component connections and attribute settings while maintaining scalability through neural network-based pattern recognition.
2Productivity
If manual configuration rules are used, then system complexity is low, but scalability and pattern discovery are limited
Solution Approach 1:
The patent replaces manual configuration rules with a graph neural network system that automatically learns patterns from historical data. This substitution significantly improves configuration efficiency and pattern discovery capabilities while the system manages complexity through standardized graph-based representations and neural network processing.
Solution Approach 2:
The patent changes the fundamental parameters of the recommendation system by transitioning from rule-based approaches to neural network-based approaches. The graph neural network processes component features and topology as numerical tensors, enabling scalable pattern recognition and automatic generation of connection and attribute recommendations without manual rule definition.
3Measurement precision
If graph neural network is used to encode component features and topology, then recommendation quality improves, but computational complexity increases
Solution Approach 1:
The patent segments the configuration problem into distinct components: the graph neural network encodes component features and topology separately, then generates recommendations based on the encoded representations. This segmentation allows the system to process complex information in manageable stages, improving recommendation accuracy while managing computational requirements through structured processing.
Data Source
AI summary
A computer-implemented method for providing recommendations, REC, concerning a configuration process is provided to configure an industrial system, SYS, the method including the steps of calculating by a trained graph neural network, GNN, scores, s, for components, c, of a set, C, of configurable component types, ct; generating recommendations, REC, for introducing at least one additional component, c, into the industrial system, SYS, on the basis of the calculated scores, s; and outputting the generated recommendations, REC, to a user by a user interface or executing the generated recommendations.