Monitoring Centre Auto-Zoom for Abnormal Event Detection
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Solution Overview
Problem
Conventional monitoring systems often fail to promptly notice and accurately assess abnormal events in monitored areas due to overwhelming sensor data, leading to potential delays in response to critical incidents such as fires or equipment malfunctions.
Innovation Solution
A monitoring centre is configured to detect abnormal events by identifying impacted sensors, triggering them to provide enhanced data, and auto-zooming the event representation for improved visibility and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensors continuously deliver sensor data to the monitoring centre, then the monitoring centre can detect abnormal events, but the amount of information displayed on monitoring screens becomes overwhelming and abnormal events may go unnoticed
Solution Approach 1:
The patent segments sensor data processing by identifying and focusing on specific sensors potentially impacted by abnormal events, rather than processing all sensor data uniformly. This segmentation allows the system to filter out irrelevant information and concentrate on critical data sources, resolving the contradiction between comprehensive monitoring and information overload.
Solution Approach 2:
The patent applies local quality by triggering enhanced sensor data delivery and auto-zooming specifically for affected areas rather than uniformly across the entire monitored space. This localized approach ensures detailed monitoring where needed while maintaining overall system efficiency, preventing information overload in non-critical areas.
2Reliability
If the monitoring centre monitors all sensor data continuously, then it can detect abnormal events, but the response time to critical incidents is delayed
Solution Approach 1:
The patent implements preliminary action by pre-identifying sensors that may be impacted by abnormal events and preparing them for enhanced monitoring. When an abnormal event is detected, these pre-identified sensors immediately begin delivering enhanced data, eliminating the delay that would occur if the system had to identify and configure sensors in real-time.
Solution Approach 2:
The patent applies dynamics by making sensor data delivery frequency adaptive rather than static. Sensors dynamically adjust their data delivery rate based on their relevance to detected abnormal events, delivering enhanced data at higher frequencies when needed and returning to normal operation when not affected, thus optimizing response time without continuous high-rate monitoring of all sensors.
3Measurement precision
If the monitoring centre triggers sensors to deliver enhanced sensor data at higher frequency, then the resolution of event representation is improved, but the use of energy and data transmission increases
Solution Approach 1:
The patent applies local quality by concentrating enhanced monitoring resources specifically on sensors impacted by abnormal events rather than uniformly enhancing all sensors. This localized enhancement achieves high measurement precision where needed while avoiding unnecessary energy consumption in unaffected areas, resolving the contradiction between accuracy and energy use.
Solution Approach 2:
The patent makes sensor operation dynamic by adjusting data delivery frequency based on event relevance. Sensors deliver enhanced data at higher frequencies only when they detect or are near abnormal events, and return to normal lower-frequency operation when not affected. This dynamic adjustment achieves high measurement precision during critical moments while significantly reducing overall energy consumption compared to continuous high-rate monitoring.
Data Source
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AI summary
A method and monitoring centre(400) for monitoring occurrence of events in a monitored area (404), wherein a monitoring equipment (402) displays information that reflects sensor data(D) reported from sensors in the monitored area. When the monitoring centre(400) detects an abnormal event, it identifies a set of sensors (S1-S3) which are potentially impacted by the event, and triggers the sensors (S1-S3) to enter a vigilance state of elevated operation and to deliver enhanced sensor data (D-e). Further,a representation (R) of the abnormal event is auto-zoomed on the monitoring equipment based on the enhanced sensor data from the sensors (S1-S3),such that resolution of the displayed representation is increased.