Architectural Drawing Analysis for Automated Access Control Specs
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Solution Overview
Problem
The process of determining proper door hardware for buildings is tedious and complex due to varying standards and regulations, and existing long-range asset monitoring is limited to GPS and cellular triangulation, lacking precision and ubiquity.
Innovation Solution
A system that analyzes architectural drawings to determine door locations and functions, applies predictive machine learning to select appropriate access control hardware, and uses long-range tags with multiple frequency communication to monitor hardware location and status, integrating with online marketplaces for acquisition and tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated analysis of architectural drawings is implemented to determine door locations and hardware specifications, then productivity and time efficiency are improved, but device complexity and implementation difficulty increase
Solution Approach 1:
The patent replaces manual mechanical processes (architects and spec writers physically analyzing drawings and selecting hardware) with an automated computer vision system using machine learning models. The system processes architectural drawings through trained ML models to automatically identify door locations, determine room functions, and specify appropriate access control hardware, eliminating the need for human expertise in these tasks.
Solution Approach 2:
The system enables self-service by allowing architectural drawings to automatically generate hardware specifications without human intervention. The trained machine learning models independently analyze drawing features, extract door information, apply building code rules, and produce complete hardware specifications autonomously, making the system serve itself rather than requiring expert operators.
2Manufacturing precision
If multiple machine learning models are trained for different door hardware categories to improve prediction accuracy, then manufacturing precision and reliability are improved, but loss of time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by pre-training multiple specialized machine learning models for different door hardware categories (electronic locks, exit devices, door closers, readers, hinges) before deployment. These models are trained in advance on historical data and architectural drawings to learn the relationships between door characteristics and appropriate hardware selections, so that during actual specification generation, the pre-trained models can quickly and accurately predict hardware requirements without requiring training at runtime.
3Measurement precision
If long-range monitoring tags with multiple frequency communication are used to track access control hardware, then measurement precision and reliability are improved, but device complexity and cost increase
Solution Approach 1:
The patent applies universality by designing monitoring tags that support multiple communication frequencies (900 MHz and 2.4 GHz) within a single device. This multi-functional tag can operate across different frequency bands, allowing the same hardware to serve multiple communication needs and work with various base stations, thereby improving tracking reliability and coverage without requiring separate specialized devices for each frequency.
Data Source
AI summary
A method according to one embodiment includes determining, by a server, a location of a door in an architectural drawing and a room function of a room secured by the door based on an analysis of the architectural drawing, determining, by the server, proper access control hardware to be installed on the door based on the room function, a category of access control hardware, and a predictive machine learning model associated with the category of access control hardware, and generating, by the server, a specification based on the determined proper access control hardware.