Semantic Context Matching for Transferable Building Automation Models
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Solution Overview
Problem
Existing building automation systems face challenges in adapting to changes in context, as pre-trained evaluation and control models are not easily transferable to new setups with different sensor configurations and data formats, leading to inefficiencies in deployment and retraining.
Innovation Solution
The method involves generating semantic-based descriptions of both the source and target contexts, using semantic matchmaking to match pre-trained models with new contexts, allowing for the selection and adaptation of suitable models, and recommending modifications to ensure effective deployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If pre-trained evaluation and control models are used for new building automation systems, then deployment time is reduced, but the models are not transferable to new setups with different sensor configurations and data formats
Solution Approach 1:
The patent transforms the model representation by encoding it in a semantic language that captures the meaning and relationships of parameters rather than their specific values. This allows the model to be transferred to new contexts by interpreting semantic relationships rather than copying specific parameter values, resolving the contradiction between rapid deployment and adaptability to different sensor configurations.
Solution Approach 2:
The patent introduces a semantic language as an intermediary layer between the pre-trained model and the target building automation system. This semantic representation acts as a mediator that translates the model's parameter relationships into a form that can be adapted to different sensor configurations and data formats without retraining, enabling both fast deployment and high transferability.
2Manufacturing precision
If manual configuration of building automation devices is performed, then system accuracy is improved, but setup time increases significantly
Solution Approach 1:
The patent performs preliminary actions by pre-training evaluation and control models on source building automation systems before deployment. These pre-trained models capture the semantic relationships and parameter interactions needed for accurate configuration, allowing the target system to inherit this knowledge and achieve accurate configuration without manual setup, thus reducing setup time while maintaining precision.
Solution Approach 2:
The patent copies the semantic structure and parameter relationships from source models to target models through semantic transformation rather than direct copying of values. This allows the target system to inherit the configurational knowledge and accuracy of source systems while adapting to different hardware configurations, eliminating the need for time-consuming manual configuration.
3Adaptability or versatility
If semantic matchmaking is used to match pre-trained models with new contexts, then model adaptability is improved, but computational complexity increases
Solution Approach 1:
The patent segments the semantic matching process into distinct components: encoding the model in semantic language, extracting semantic features, and performing matching operations. This segmentation allows each component to be optimized independently, reducing overall computational complexity while maintaining high adaptability for matching pre-trained models with new building automation contexts.
Data Source
AI summary
A method for providing an evaluation and control model for controlling target building automation devices of a target building automation system includes: providing a plurality of pre-trained source evaluation and control models for controlling source building automation devices of a source building automation system; generating for each pre-trained source evaluation and control model a semantic based description of a context in which the model was trained; generating a semantic based description of the context of the target building automation system; retrieving the semantic based description of the context of each pre-trained source evaluation and control model of the plurality of pre-trained source evaluation and control models; and matching the generated semantic based description of the context of the target building automation system and the semantic based description of the context in which the pre-trained source evaluation and control models were trained by using a semantic matchmaking concept.