AI Floor Plan Parsing for Fire Safety Configuration Lists
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Solution Overview
Problem
Existing methods for creating configuration lists of fire safety system elements from building floor plans are inefficient and require manual re-generation of CAD-conformant floor plans with sparse information, lacking automation.
Innovation Solution
An AI-driven method and system that extracts geometrical data patterns from ichnographical building floor plans, associates them with metadata using self-learning algorithms, and compiles a structured alphanumerical configuration list of fire safety system elements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used to create configuration lists, then accuracy and expertise can be applied, but time consumption and labor intensity increase
Solution Approach 1:
The system enables self-service automation where the AI processor automatically extracts geometrical data, generates metadata, and creates configuration lists without requiring manual intervention. The self-learning interpreter rule generator continuously improves the system's capability through autonomous learning from training data, eliminating the need for manual expertise while maintaining high accuracy.
Solution Approach 2:
The patent replaces the manual mechanical process of technicians reviewing floor plans and creating configuration lists with an automated AI-based system. The AI processor uses machine learning models to automatically interpret ichnographical instances, extract spatial information, and generate configuration lists, substituting human manual work with intelligent automation.
2Productivity
If automated algorithms are used to speed up configuration list creation, then productivity increases, but complexity of the system increases
Solution Approach 1:
The system segments the complex task of configuration list creation into distinct modular components: the AI processor for data extraction, the interpreter rule generator for metadata generation, and the configuration list compiler for final output. This modular architecture manages system complexity by dividing functions into independent, manageable modules that can be developed and maintained separately.
Solution Approach 2:
The patent introduces an intermediary layer of self-learning algorithms and trained machine learning models that act as mediators between the raw ichnographical floor plan data and the final configuration list. This intermediary layer handles the complexity of interpretation and transformation, simplifying the overall system architecture while maintaining high productivity.
3Adaptability or versatility
If CAD-conformant floor plans with sparse information are used, then standardization is achieved, but information completeness is lost
Solution Approach 1:
The system creates a virtual copy or digital representation of the floor plan information by extracting geometrical data patterns from the ichnographical instance and transforming them into structured metadata. This copying process preserves all necessary spatial and dimensional information in a standardized digital format that can be processed automatically without losing critical details.
Solution Approach 2:
The patent transforms the two-dimensional ichnographical floor plan representation into a multi-dimensional structured data model with multiple attributes and metadata layers. This dimensional transformation enriches the standardized floor plan data with additional information dimensions, including spatial relationships, element properties, and configuration parameters, thereby recovering information completeness while maintaining standardization.
Data Source
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AI summary
The present disclosure relates to a method for creating a configuration list of fire safety system elements from an ichnographical instance of a building floor plan. The method comprises receiving, by an artificial intelligence, AI, processor of an AI module, a data file containing an ichnographical instance of a building floor plan. The method further comprises extracting, by the AI processor, geometrical data patterns in the ichnographical instance of the building floor plan contained in the received data file. In addition, the method comprises associating, by the AI processor, the extracted geometrical data patterns with metadata indicating fire safety system elements according to an operative reference rule set derived by self-learning performed by an interpreter rule generator of the AI module. Further, the method comprises compiling a structured alphanumerical configuration list of fire safety system elements corresponding to the building floor plan based on the associated metadata generated by the AI processor. The present disclosure further relates to a system for creating a configuration list of fire safety system elements from an ichnographical instance of a building floor plan.