AI Elevator Car Routing to Reduce Unnecessary Stops
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Solution Overview
Problem
Modern commercial elevators often waste time and resources due to inefficient routing, inability to recall cars, and limited emergency assistance options.
Innovation Solution
An AI model trained with sensor data and historical routes determines the shortest route for an elevator car, resolving conflicts, and provides emergency assistance when needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional elevator routing is used, then the system is simple to operate, but the elevator makes unnecessary stops and wastes time
Solution Approach 1:
The AI model is trained in advance with historical sensor data and route information to learn optimal routing patterns. This preliminary training enables the system to make intelligent routing decisions without real-time complex calculations, reducing travel time while maintaining manageable operational complexity
Solution Approach 2:
The system continuously collects sensor data from the elevator car and uses it to train and refine the AI model. This feedback loop allows the system to learn from actual operating conditions and improve routing efficiency over time, balancing performance improvement with system complexity
2Ease of operation
If the elevator cannot be recalled after dispatch, then the control system is simple, but passengers may be forced to travel to floors they no longer wish to visit
Solution Approach 1:
The elevator routing system transitions from a static, predetermined path to a dynamic, AI-determined route that can be adjusted based on real-time sensor data and passenger needs. This dynamic approach allows the elevator to be recalled or rerouted to accommodate changing passenger requirements while maintaining system manageability
3Adaptability or versatility
If traditional routing is used, then the system is reliable and predictable, but the elevator cannot adapt to changing conditions or provide emergency assistance
Solution Approach 1:
The AI model autonomously analyzes sensor data and determines optimal routes and emergency responses without requiring external intervention. This self-service capability enables the system to adapt to changing conditions and provide emergency assistance while maintaining operational reliability through consistent AI-driven decision-making
Data Source
AI summary
A method, apparatus, and computer program product for protecting electronic devices from obstructed voice commands. The method includes receiving, from one or more sensors, sensor data associated with an elevator car and training an artificial intelligence (“AI”) model with a set of preset parameters, archived data from the one or more sensors and corresponding archived data depicting a historical route traveled by the elevator car. The method includes determining, via the AI model, a route for the elevator car based at least in part on the sensor data, wherein the determined route is a shortest route that satisfies the set of preset parameters and transporting the elevator car according to the determined route.