Alarm Risk Scoring with Sensor-Video Context Filtering
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Solution Overview
Problem
Monitoring systems in controlled environments face sensor and event information overload, leading to inefficiencies and overlooked vital alerts due to cumbersome processing of inconsequential data.
Innovation Solution
Implementing computer vision and machine learning (ML) to distinguish between events requiring immediate attention and those that do not, using sensors and video analysis to generate accurate and concise alarm information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensor information and rules are used to generate alarms, then alarm detection capability is provided, but information overload occurs and vital alerts are overlooked
Solution Approach 1:
The system extracts only the most critical alarm attributes and context information from the overwhelming sensor data, separating essential alert details from inconsequential data. This extraction process filters out non-critical information while preserving vital alert characteristics for operator attention.
Solution Approach 2:
The patent introduces an intermediary alarm management system that sits between sensor data collection and operator notification. This intermediary layer processes, prioritizes, and contextualizes alarm information, preventing raw data overload from reaching operators while maintaining reliable detection capability.
2Reliability
If all sensor information is processed, then comprehensive monitoring is achieved, but processing efficiency decreases and operator workload increases
Solution Approach 1:
The system extracts and processes only critical alarm attributes and essential context information, removing unnecessary data processing steps. This selective extraction maintains comprehensive monitoring of vital parameters while significantly reducing overall processing load and improving efficiency.
Solution Approach 2:
The patent applies different processing qualities to different alarm attributes - critical attributes receive detailed analysis and contextual processing, while non-critical attributes receive minimal or no processing. This local quality differentiation maintains comprehensive monitoring where needed while reducing unnecessary processing elsewhere.
3Stability of the object's composition
If preconfigured rules are used for alarm generation, then alarm consistency is maintained, but inability to prioritize events occurs
Solution Approach 1:
The system performs preliminary prioritization and contextual analysis of alarm attributes before presenting them to operators. By pre-processing alarm information to determine urgency and significance, the system maintains rule-based consistency while enabling effective event prioritization for operator response.
Solution Approach 2:
The patent changes the parameters used for alarm evaluation by incorporating contextual information and prioritization metrics alongside traditional rule-based criteria. This parameter expansion maintains consistency through structured evaluation while adding prioritization capability through new assessment dimensions.
Data Source
AI summary
A system may be configured to provide alarm risk score intelligence and analysis. In some aspects, the system may receive sensor information captured by one or more sensors, the sensor information indicating activity within a controlled environment, and determine an event based on the sensor information. Further, the system may receive one or more video frames from one or more video capture devices and determine context information based on the one or more video frames. Additionally, the system may modify the event based on the context information to generate an alarm and transmit a notification identifying the alarm to a monitoring device.