Visual Scene Monitoring for Amusement Ride Anomaly Detection
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Solution Overview
Problem
Conventional monitoring and maintenance systems in amusement parks and entertainment venues are inadequate due to human error and lack of automated monitoring, failing to detect maintenance and user experience issues effectively, especially in complex attractions involving multiple sensory and interactive elements.
Innovation Solution
A system utilizing a network of communicatively coupled sensors, machine learning, and multidimensional modeling for anomaly detection, predictive maintenance, and automated corrective actions, which collects and analyzes data from various sources to provide robust and intelligent monitoring and maintenance, including visual, audio, and environmental aspects, and uses AI to coordinate sensor data and equipment performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional monitoring systems are used in amusement parks, then the system simplicity is maintained, but the detection precision and reliability of maintenance issues deteriorate due to human error and lack of automated monitoring
Solution Approach 1:
The patent replaces manual monitoring systems with automated sensor-based monitoring systems. Multiple sensors (vibration, temperature, acoustic, visual) automatically detect and monitor ride vehicle components, replacing human operators and improving detection precision while eliminating human error in maintenance issue detection.
Solution Approach 2:
The patent introduces communication hubs as intermediaries that collect data from multiple sensors and relay information to central monitoring systems. These hubs act as mediators between the physical sensor layer and the digital processing layer, enabling sophisticated monitoring without requiring direct complex connections between all components.
2Reliability
If multiple sensors and automated monitoring systems are deployed, then the reliability of maintenance detection is improved, but the device complexity and cost increase
Solution Approach 1:
The patent divides the monitoring system into segmented functional modules: vibration sensors, temperature sensors, acoustic sensors, visual sensors, communication hubs, and central processing systems. Each module performs a specific function, and they work together through standardized interfaces, making the complex system manageable and maintainable while achieving high reliability through redundancy and specialization.
3Productivity
If manual monitoring methods are used, then the ease of operation is maintained, but the productivity and responsiveness to anomalies deteriorate due to delayed detection and reaction times
Solution Approach 1:
The monitoring system is designed to autonomously detect anomalies, generate alerts, and notify relevant personnel without requiring continuous manual intervention. The system self-monitors ride vehicle components, automatically processes sensor data, and triggers maintenance protocols when thresholds are exceeded, significantly improving detection speed while maintaining operational simplicity through automation.
4Loss of information
If comprehensive sensor networks are implemented across all attractions, then the coverage and detection capability are improved, but the loss of information and data management burden increase
Solution Approach 1:
The patent implements a universal communication hub and data processing platform that handles multiple sensor types (vibration, temperature, acoustic, visual) and multiple attraction types through standardized protocols and interfaces. This multi-functional approach ensures comprehensive information collection across all attractions while simplifying data management through a unified system architecture that processes diverse data streams consistently.
Data Source
AI summary
The application is directed to systems and methods of performing anomaly detection, predictive maintenance, and anomaly correction in an amusement park experience. A method may include receiving, via a sensor network, multiple layers of first sensor data indicative of characteristics of the experience and generating a profile of the experience based on the first sensor data, wherein the profile includes a baseline and a threshold. The method may also include receiving second sensor data and third sensor data via the sensor network, determining, in response to identifying characteristics of the second sensor data that deviate from the baseline but do not exceed the threshold, that the experience is operating properly, and performing a particular corrective action in response to identifying characteristics of the third sensor data that deviate from the baseline and exceed the threshold.