Emergency Dispatch Event Correlation for False Alarm Filtering
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Solution Overview
Problem
Existing emergency dispatch systems face challenges in efficiently correlating and automating the dispatch of emergency responders based on diverse data inputs from alarms, sensors, and mobile devices, often leading to inefficiencies and potential false alarms.
Innovation Solution
An apparatus and method for correlating emergency events using event correlation logic and false alarm detection logic, applying emergency network dispatch rules through machine learning, and automatically dispatching responders based on geographic boundaries and event types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated dispatch systems are implemented to improve response efficiency, then productivity increases, but device complexity increases due to need for event correlation logic and false alarm detection logic
Solution Approach 1:
The system divides the complex dispatch process into separate functional modules: event correlation logic for processing multiple event types, false alarm detection logic for filtering spurious events, and automated dispatch logic for executing responses. This segmentation allows each module to handle specific tasks independently, reducing overall system complexity while maintaining high productivity.
Solution Approach 2:
The patent introduces an intermediary event correlation logic layer that sits between raw sensor/alarm inputs and the automated dispatch system. This intermediary processes and correlates events from multiple sources, filtering and preparing data before it reaches the dispatch logic, thereby simplifying the overall system architecture while enabling efficient automated response.
2Measurement precision
If event correlation logic is added to process diverse data inputs, then measurement precision improves, but device complexity increases
Solution Approach 1:
The system merges multiple data inputs from diverse sources (alarms, sensors, mobile devices) into a unified event correlation processing stream. By combining these data sources and processing them through a single correlation logic layer, the system achieves precise event identification without proportionally increasing complexity, as the merged processing handles all data types through standardized correlation rules.
3Reliability
If false alarm detection logic is implemented to reduce false alarms, then reliability improves, but device complexity increases
Solution Approach 1:
The false alarm detection logic incorporates feedback mechanisms that continuously learn from dispatch outcomes and event patterns. By analyzing historical data and adjusting detection criteria based on actual performance, the system improves reliability over time without requiring increasingly complex detection algorithms, as the feedback loop optimizes existing logic rather than expanding it.
Data Source
AI summary
An emergency data manager includes a mapping module that is operative to generate a map view in a cloud-based user interface provided to a public safety answering point (PSAP) by the emergency data manager. The map view displays location indicators for emergencies being handled by the PSAP. Machine learning trained logic is operatively coupled to the mapping module and is operative to correlate incoming emergency data and provide contextual data to PSAP dispatchers via the cloud-based user interface. The contextual data includes time, location, and event type. The machine learning trained logic may be further operative to provide a dispatch recommendation based on the contextual data, or based on contextual data and a set of dispatch rules. The machine learning trained logic may be further operative to provide a simulation of an experienced PSAP call taker or dispatcher.