Connected Lighting Sensors for Predictive Queue Forecasting
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Solution Overview
Problem
Current predictive queue management (PQM) systems lack accuracy and are cost-prohibitive due to the reliance on people counting cameras that only cover limited areas, failing to forecast occupancy status and customer flow dynamics across entire spaces.
Innovation Solution
Implement a connected lighting system with embedded sensors (PIR, SPT, RF) to capture motion and thermal data, using machine learning models for queue volume prediction, and optimize sensor placement with deep learning to minimize cost and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If people counting cameras are used for PQM, then queue volume prediction can be obtained, but installation cost and power consumption increase significantly
Solution Approach 1:
The patent replaces camera-based optical measurement systems with sensor-based detection systems. Sensors detect physical quantities such as weight, pressure, or proximity to identify customer presence and queue formation, substituting the mechanical/optical camera system with a more energy-efficient sensor system that achieves the same measurement goal without high power consumption
Solution Approach 2:
The patent employs low-cost sensor devices instead of expensive camera systems. These sensors are inexpensive, consume minimal power, and can be deployed widely throughout the retail space to provide comprehensive queue monitoring without the high installation and operational costs associated with camera-based systems
2Loss of information
If cameras are deployed to monitor entire space, then occupancy status can be forecasted, but installation cost increases
Solution Approach 1:
The patent divides the retail space into multiple monitoring zones, each equipped with simple sensor devices. Instead of using expensive cameras to cover the entire space, the system segments the area and uses distributed low-cost sensors to detect customer presence in each zone, achieving comprehensive coverage through economical means
Solution Approach 2:
The patent introduces sensor devices as intermediary detection elements between customers and the PQM system. These sensors act as mediators that detect customer presence and transmit data to the central system, providing a cost-effective alternative to direct camera monitoring while maintaining information flow for occupancy forecasting
3Measurement precision
If more sensors are deployed throughout the space, then prediction accuracy improves, but power consumption and cost increase
Solution Approach 1:
The patent applies sensors selectively at specific locations where queue formation is most likely to occur, such as near checkout counters and high-traffic areas. Instead of uniformly distributing sensors throughout the entire space, the system concentrates detection resources at critical points, maintaining prediction accuracy while minimizing the total number of sensors and their power consumption
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Achieves accurate and cost-effective PQM by determining optimal sensor locations, reducing processing time and power usage while maintaining prediction performance, thereby improving customer satisfaction and operational efficiency.
Implementation Method 1
at least one of the plurality of sensors is a passive infrared (PIR) sensor configured to capture motion data
Implementation Method 2
at least one of the plurality of sensors is a single pixel thermopile (SPT) sensor configured to capture thermal data indicative of occupancy of individuals
Data Source
AI summary
A system for predictive queue management of a monitored area, including a controller having a processor and sensors installed within a connected lighting system at optimal locations, is provided. The optimal locations are generated by a sensor selection model based on selection training data and potential sensor locations. The sensors capture optimized sensor data corresponding to 2024/037908 individuals in the monitored area, and may include PIR sensors, SPT sensors, and/or RF sensors. The processor then generates, based on the optimized data and a forecasting model, a queue volume prediction including a number of individuals that will need a service in the monitored area during a predetermined future time window. The processor then generates, based on recommender inputs including at least the queue volume prediction, a recommendation including a number of queues needed to process the queue volume prediction.