Access Control Hardware Selection From Architectural Drawings
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Solution Overview
Problem
The process of determining appropriate door hardware for commercial buildings is tedious and complicated by varying standards and regulations, often requiring expertise not possessed by architects, and there is a need for long-range monitoring of access control hardware.
Innovation Solution
A system that analyzes architectural drawings to determine hardware locations, uses predictive models to select appropriate access control hardware, and employs long-range tags to monitor hardware positions, leveraging cloud servers, base stations, and hardware tags for tracking and alerting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If architects manually select access control hardware for each door, then they can ensure proper hardware selection, but the process becomes time-consuming and requires specialized expertise
Solution Approach 1:
The system enables self-service by automatically analyzing architectural drawings and generating hardware specifications without requiring architect intervention. The automated system processes door locations, room functions, and regulatory requirements to produce complete hardware specifications, freeing architects from manual selection tasks while maintaining accuracy through algorithmic decision-making
Solution Approach 2:
The patent replaces the manual mechanical process of hardware selection with an automated computational system. The system uses image processing to extract door locations, applies rule-based logic to determine hardware types based on room functions and regulations, and generates specifications automatically, substituting human expertise with automated algorithms
2Length of moving object
If traditional GPS and triangulation methods are used for device tracking, then long-range monitoring is achieved, but the system lacks precision for local asset tracking
Solution Approach 1:
The tracking system is segmented into multiple operational modes with different ranges and precision characteristics. The system divides the monitoring space into local area (for precise tracking near base stations) and remote area (for long-range monitoring), selecting appropriate tracking methods based on the device's location and requirements
Solution Approach 2:
The system dynamically switches between different tracking methods based on real-time conditions. When a device is near a base station, the system uses high-precision local tracking; when far from base stations, it transitions to long-range GPS-based tracking, creating a dynamic adaptive tracking system that optimizes both precision and range
Data Source
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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.