Event Venue Environmental Control Using Predicted Crowd Size
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Solution Overview
Problem
Conventional venue environmental systems are static and reactive, often requiring manual intervention to achieve occupant comfort, and are inefficient in large venues due to lack of proactive control over environmental conditions based on crowd size and preferences.
Innovation Solution
A machine learning system that infers crowd size and demographics from social networks, trains on comfort data, and dynamically adjusts environmental conditions such as temperature, lighting, and sound to enhance occupant comfort through a comfort model and controller.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional static environmental systems are used in large venues, then device complexity is reduced, but occupant comfort and responsiveness to crowd conditions deteriorate
Solution Approach 1:
The system performs preliminary actions by predicting crowd size and characteristics before the event occurs, using social media data analysis and machine learning models. This allows environmental conditions to be pre-adjusted based on anticipated occupancy, improving responsiveness without requiring complex real-time sensing infrastructure throughout the venue.
Solution Approach 2:
The patent introduces an intermediary machine learning system that mediates between social media data and environmental control systems. This intermediary processes crowd prediction data and translates it into environmental adjustment parameters, simplifying the overall system architecture while enabling adaptive comfort control based on predicted occupancy patterns.
2Ease of operation
If manual intervention is required to adjust environmental conditions, then ease of operation is reduced, but system reliability improves
Solution Approach 1:
The system implements self-service by automatically adjusting environmental conditions based on crowd predictions without requiring manual intervention. The machine learning model continuously processes social media data and autonomously controls environmental systems, improving ease of operation while maintaining reliability through feedback loops and continuous learning from actual occupancy data.
Solution Approach 2:
The patent incorporates feedback mechanisms where actual occupancy data and environmental responses are fed back into the machine learning model. This feedback loop allows the system to learn from past performance, refine its predictions, and improve reliability over time while maintaining automatic operation.
3Productivity
If reactive environmental control is used, then response time to crowd changes is improved, but productivity and efficiency deteriorate
Solution Approach 1:
The system performs preliminary environmental adjustments based on predicted crowd size before occupants arrive or before the event begins. By analyzing social media data in advance, the system pre-conditions the venue environment, eliminating the time lag associated with reactive control while improving overall system efficiency through proactive optimization.
Data Source
AI summary
A venue occupant comfort system, comprises a processor that stores computer executable components stored in memory. A plurality of sensors sense ambient conditions associated with exterior and interior conditions of a venue. A context component infers or determines context of an occupant of the venue. A crowd estimation component infers, based at least in part on mining social networks, size of crowd expected at the venue. A comfort model component implicitly and explicitly trained on occupant comfort related data analyzes information from the plurality of sensors, the crowd estimation component and context component. A comfort controller adjusts environmental conditions of the venue based at least in part on output of the comfort model component. The adjustments to venue environment can optionally be differentiated by zone.