Elevator Car Allocation for Passengers With Variable Space Needs
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Solution Overview
Problem
Special passengers requiring additional space, such as those with large luggage or in wheelchairs, often face situations where available elevator space is insufficient, leading to delays or inability to use the elevator.
Innovation Solution
An elevator system with passenger detection, identification, and allocation devices that dynamically assess passenger space requirements and car availability to optimize car allocation, using AI recognition and compensation factors to ensure efficient space utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional elevator allocation systems are used, then the system is simple to operate, but passengers with large items cannot be allocated to appropriate cars, leading to increased waiting time
Solution Approach 1:
The system performs preliminary actions by capturing passenger images in advance at the entrance, identifying space requirements before the passenger reaches the elevator, and pre-allocation to appropriate cars. This allows the system to prepare allocation decisions ahead of time, reducing actual waiting time while managing complexity through automated preliminary processing.
Solution Approach 2:
The allocation system segments passengers into different categories (level 0 for standard passengers, levels 1-n for passengers with items of varying sizes). This segmentation allows the system to handle different passenger types with specific allocation rules, improving time management for special passengers while maintaining manageable system complexity through structured classification.
2Adaptability or versatility
If the elevator allocates space based on general availability, then the allocation process is fast, but passengers with specific space requirements cannot find suitable cars
Solution Approach 1:
The system applies local quality by providing differentiated allocation rules for different passenger types. Standard passengers (level 0) receive basic allocation, while passengers with items (levels 1-n) receive tailored allocation based on their specific space requirements. This localized adaptation ensures suitable car matching without significantly reducing overall allocation efficiency through structured decision pathways.
Solution Approach 2:
The system changes parameters by classifying space requirements into discrete levels (0 to n) based on item size, and matching these against car availability parameters. This parameter transformation converts the complex continuous problem of space matching into a manageable discrete classification system, improving adaptability while maintaining allocation efficiency through standardized parameter comparison.
3Measurement precision
If the system classifies space requirements into multiple levels, then allocation accuracy improves, but the classification complexity increases
Solution Approach 1:
The system segments the continuous space requirement into discrete levels (0 for no large items, 1-n for progressively larger items). This segmentation improves measurement precision by providing granular classification for different item sizes, while controlling classification complexity through a structured hierarchical system that can be implemented with clear decision rules.
Solution Approach 2:
The system applies partial classification action by focusing detailed multi-level classification only on passengers who carry items exceeding predetermined size, while using simpler classification for standard passengers. This selective approach improves accuracy for the specific subgroup that needs it, without unnecessarily increasing overall system complexity for all passengers.
4Productivity
If the system monitors remaining space dynamically in each car, then allocation optimization improves, but the monitoring and data processing complexity increases
Solution Approach 1:
The system achieves universality by using the same car detection device and monitoring mechanism across all elevator cars. This multi-functional approach allows real-time space monitoring in multiple cars simultaneously, improving allocation optimization through comprehensive data collection, while controlling monitoring complexity by using identical standardized sensors and processing methods in each car.
Solution Approach 2:
The system maintains continuous monitoring of remaining space in each car, ensuring up-to-date allocation data is always available. This continuous useful action improves productivity by enabling real-time optimization decisions, while managing data processing complexity through steady-state monitoring routines that process information continuously at manageable rates rather than through complex intermittent sampling.
Data Source
Figure 1A~1D
Figure 2
AI summary
An elevator system, including: a passenger detection device, which is arranged at a predetermined position leading to an elevator and is configured to take an image of a passenger passing by the predetermined position; an identification device, configured to receive the image from the passenger detection device and identify a space requirement of the passenger according to the image; a car detection device, arranged in each car and configured to dynamically monitor a remaining space in each car; and a passenger allocation device, configured to allocate the passenger to an appropriate car according to the space requirement of the passenger and the remaining space in each car. The elevator system can optimize the allocation of cars.