Event Detection System Using Object Models for Real-Time Monitoring
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Solution Overview
Problem
Existing event data recorder (EDR) systems lack the capability to efficiently detect and monitor events and precursors to events in real-time, and provide effective interfaces for managing and viewing recorded data.
Innovation Solution
An event detection system comprising multiple sensor devices that generate and process data streams, using object models to define procedures for event detection, and streaming data to a server system for further analysis and visualization, incorporating features like neural networks and stereoscopic inference models for event recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensor data is continuously recorded and monitored in real-time, then event detection capability is improved, but data processing complexity and energy consumption increase
Solution Approach 1:
The system performs preliminary actions by continuously monitoring sensor data streams and detecting events in real-time before comprehensive analysis is required. This allows the system to identify potential incidents early and trigger targeted processing only when necessary, reducing overall processing complexity while maintaining detection reliability.
Solution Approach 2:
The patent introduces an intermediary event detection layer that sits between raw sensor data collection and full data processing. This intermediary layer filters and pre-processes data streams, identifying significant events before they undergo comprehensive analysis, thereby reducing the complexity burden on the main processing system.
2Measurement precision
If multiple sensor devices are deployed to capture comprehensive data, then event detection accuracy is improved, but system complexity and data management difficulty increase
Solution Approach 1:
The system segments the monitoring function across multiple independent sensor devices, each capturing specific data streams. This segmentation allows for specialized optimization of each sensor type while maintaining overall system accuracy. The modular architecture reduces management complexity by allowing independent configuration and processing of each sensor's data stream.
Solution Approach 2:
The patent implements a universal data processing framework that handles multiple sensor types through common procedures and object models. This multi-functional approach allows the same system architecture to process diverse sensor data streams uniformly, reducing the complexity that would otherwise arise from handling each sensor type separately.
3Loss of time
If real-time event monitoring is implemented, then response time to incidents is improved, but energy consumption increases
Solution Approach 1:
The system employs periodic action by monitoring sensor data streams at optimized intervals rather than continuously processing all data. Event detection procedures are triggered periodically or event-driven, allowing the system to maintain real-time monitoring capability while consuming energy only when necessary to process and respond to actual events.
Solution Approach 2:
The patent utilizes parameter changes by adjusting monitoring intensity and processing frequency based on system state and event likelihood. The object models allow dynamic modification of detection parameters, enabling the system to maintain fast response capability while reducing energy consumption during periods of low activity by lowering the intensity of monitoring and processing operations.
Data Source
AI summary
An event detection system to operations that include: detecting an event based on at least a portion of the sensor data, the event comprising a plurality of event attributes; determining one or more event attributes from the plurality of event attributes of the event transgress a threshold value; and causing display of a notification that includes a presentation of at least the portion of the sensor data in response to the determining that the one or more event attributes from among the plurality of event attributes of the event transgress the threshold value.