Parameter Setting Recommender for Unknown Plant Operating States
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Solution Overview
Problem
Current methods for setting adjustable parameters in technical plants and business transactions require domain knowledge and are complex, often relying on manual decisions that are not transparent or efficient, especially when dealing with new operating states or customer-specific adaptations.
Innovation Solution
A computer-implemented recommendation method using a recommender system based on machine learning and collaborative filtering, which processes input data to provide accurate and reliable recommendations for adjustable parameters through representation learning and multi-output Gaussian processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual decision-making based on domain knowledge is used for setting adjustable parameters, then accuracy of settings can be maintained, but productivity and speed of implementation deteriorate
Solution Approach 1:
The system enables automatic self-service by using machine learning models to autonomously determine optimal parameter settings without requiring manual expert intervention. The recommender system processes input data and generates settings automatically, allowing the system to serve itself in terms of configuration decisions.
Solution Approach 2:
The patent replaces the mechanical/manual process of expert decision-making with an automated information processing system. Machine learning algorithms substitute for human experts, transforming the manual cognitive process into an automated computational process that maintains accuracy while dramatically improving speed.
2Productivity
If automated decision-making systems are implemented for setting adjustable parameters, then productivity and speed improve, but reliability and accuracy may deteriorate due to lack of domain knowledge
Solution Approach 1:
The system performs preliminary action by training machine learning models in advance on historical data and expert knowledge. This pre-training phase captures domain knowledge and patterns, enabling the automated system to make reliable decisions during operation without requiring real-time expert intervention.
Solution Approach 2:
The system implements feedback mechanisms where the recommender system's outputs are evaluated and used to continuously improve the underlying machine learning models. This closed-loop approach ensures that the automated system learns from its performance and maintains high reliability over time.
3Measurement precision
If complex data processing methods are used to handle sparse and high-dimensional data, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the complex data processing task into distinct modules: data preprocessing, feature extraction, machine learning model processing, and recommendation generation. This modular approach handles sparse and high-dimensional data systematically while keeping each component manageable and well-defined.
Data Source
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AI summary
The invention refers to a recommender system and method for recommending a setting of an adjustable parameter of an object for an application. For example, the object can be a device of a plant and the application corresponds to an operating state of the plant. The adjustable parameter can be a configuration parameter which requires a certain setting depending on the operating state of the plant. The invention suggests a method based on a machine learning approach to recommend such a setting, even in case of a previously unknown operating state.