Vending Robot Scheduling via Population Density Analysis
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Solution Overview
Problem
Conventional vending machines in scenic spots lack intelligence, leading to inefficiencies such as queuing and insufficient supply due to inability to adjust based on real-time population density and purchase needs.
Innovation Solution
An intelligent scheduling system comprising video monitors for population density analysis and a scheduling control server that allocates vending robots to regions based on population data, enabling them to autonomously move to high-demand areas for optimal goods distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional fixed vending machines are used in scenic spots, then the device structure is simple and easy to deploy, but they cannot adjust according to population density and purchase needs in real time, resulting in queuing phenomena and insufficient supply in high-demand areas
Solution Approach 1:
The patent applies dynamics by transforming fixed, static vending machines into mobile, dynamic vending robots that can autonomously move between different locations. The system continuously monitors population density and purchase demands in real-time, dynamically adjusting the position and distribution of vending robots to match changing tourist patterns, thereby resolving the contradiction between adaptability and device complexity
Solution Approach 2:
The system implements feedback mechanisms through video monitors that continuously capture population images, image processing systems that analyze population density, and a central processor that receives purchase demand information. This closed-loop feedback enables the system to automatically adjust vending robot positions based on real-time conditions, achieving high adaptability without requiring complex manual intervention
Solution Approach 3:
The vending robots are equipped with autonomous navigation capabilities, allowing them to self-direct to target locations without human intervention. The system performs self-service by automatically analyzing population data, determining optimal positions, and executing relocation, thereby managing complexity internally while maintaining simplicity from the user perspective
2Productivity
If a fixed number of vending machines are deployed, then the deployment cost and management complexity are low, but they cannot meet the varying purchase demands in different regions with different population densities
Solution Approach 1:
The system dynamically adjusts the spatial distribution of vending robots based on real-time population density measurements and purchase demand analysis. By continuously relocating robots to high-demand areas, the system maximizes goods supply volume where needed most and eliminates queuing time through strategic positioning, directly resolving the contradiction between productivity and time loss
Solution Approach 2:
The system performs preliminary actions by proactively positioning vending robots in anticipated high-demand areas before peak tourist periods occur. Through continuous monitoring of population flow patterns and historical data analysis, the system preemptively deploys robots to locations where demand is expected to surge, preventing queuing formation and ensuring immediate goods availability
Data Source
AI summary
The present application provides an intelligent scheduling system for vending robots and a corresponding method. The system comprises: at least one video monitor, configured to acquire a population image of a plurality of regions and obtain population density information of the plurality of regions by performing image processing on the population image; and a scheduling control server configured to allocate at least one vending robot to the plurality of regions according to the population density information of the plurality of regions and transmit, to the allocated at least one vending robot, a scheduling instruction which specifies a position of a region to which it is allocated, wherein the at least one vending robot is configured to autonomously move to the position of the region to which it is allocated for selling goods according to the scheduling instruction which specifies the position of the region from the scheduling control background server.