Attraction Control With Neural Networks for Dynamic Ride Profiles
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Solution Overview
Problem
Traditional ride systems in amusement parks have rigid operating requirements and difficult testing methods, failing to adapt to dynamic and changing environments, leading to suboptimal visitor experiences and potential safety issues due to inaccurate operation standards based on historical data.
Innovation Solution
Implementing a predictive engine using multilayer perceptron neural networks for supervised machine learning to determine a dynamic ride profile, allowing real-time adjustments based on visitor conditions and environmental changes, overriding initial alerts with recommendations for enhanced visitor experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional rigid operating requirements are used for ride systems, then safety and control are maintained, but adaptability to dynamic environments deteriorates
Solution Approach 1:
The patent applies dynamics by transitioning from rigid, static operating requirements to dynamic, adaptive ride profiles that change in real-time based on environmental conditions. The machine learning model continuously updates ride parameters (speed, position, timing) according to detected obstacles or changes, allowing the system to maintain both safety and adaptability simultaneously.
Solution Approach 2:
The system implements feedback mechanisms where sensors continuously monitor the environment, feed data to the machine learning model, which then adjusts the ride profile accordingly. This closed-loop feedback enables the system to adapt to dynamic conditions while maintaining safety through constant monitoring and adjustment.
2Measurement precision
If historical data is used to establish operation standards, then system simplicity is maintained, but accuracy in dynamic conditions deteriorates
Solution Approach 1:
The patent replaces traditional mechanical/rules-based systems with a machine learning-based intelligent system. Instead of relying on pre-programmed operation standards, the system uses neural networks to learn from historical data and adapt to new conditions, significantly improving measurement precision while managing complexity through automated learning processes.
Solution Approach 2:
The system changes parameters by transitioning from fixed operation standards to dynamically adjusted ride profiles. The machine learning model modifies various ride parameters (speed, position, timing) based on real-time environmental conditions, enabling high accuracy in dynamic conditions without requiring overly complex manual control systems.
3Ease of operation
If rigid testing methods are used for ride systems, then safety is ensured, but ease of operation and adaptability deteriorate
Solution Approach 1:
The machine learning system performs self-service by automatically learning from historical data and continuously improving its own performance. The system autonomously adjusts ride profiles without requiring manual reconfiguration for different conditions, making operation easier while maintaining safety through automated safety checks and adaptive control.
Data Source
AI summary
A controller is used to provide a dynamic ride profile for a ride system. The controller is used to access a data set indicative of conditions in an environment of the ride system in response to an alert indicative of a hindrance for the ride system in the environment. The controller is used to determine a classification from a plurality of classifications for the environment, via a predictive engine trained using supervised machine learning, based on the data set and a ride profile of the ride system. The controller is used to determine one or more recommendations, via the predictive engine, based on the data set and the classification and visually or audibly presenting the classification with the one or more recommendations.