Real-time Emergency Management System with Hierarchical Feedback
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Solution Overview
Problem
Existing risk prevention and emergency management systems are inefficient in adapting to unexpected events and do not consider the natural evolution of events like fires or terrorist attacks, relying on probability-based methodologies that lack real-time feedback from agents or sensors at the event site.
Innovation Solution
A real-time prevention and emergency management system that collects active feedback from the event site, uses intelligent automatic methodologies to detect suspicious behavior, and generates automatic threat classifications and response directives, adapting to specific scenarios through a network-based architecture with Central and Local Units, Agent Entities, and an event base-model database.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If probability-based risk measurement matrix is used, then risk assessment can be performed, but the system cannot adapt effectively and in reasonable time to unexpected events
Solution Approach 1:
The system implements active feedback loops where sensors continuously monitor event evolution and feed real-time data back to the risk assessment engine. This allows the system to dynamically adjust probability calculations based on actual event progression rather than relying on static pre-defined scenarios, enabling effective adaptation to unexpected events while maintaining rapid response times.
Solution Approach 2:
The risk measurement matrix is transformed from a static probability-based model to a dynamic system that continuously updates risk assessments as events evolve. The system adapts its parameters and calculations in real-time based on incoming sensor data and event progression, allowing it to respond effectively to unexpected situations without time loss.
2Loss of information
If probability-based methodologies are used, then risk levels can be inferred, but the system lacks real-time feedback from agents or sensors at the event site
Solution Approach 1:
The system deploys a network of sensors and agent entities at event sites that continuously collect and transmit real-time data back to the central processing unit. This feedback mechanism ensures that risk assessments are based on actual current conditions rather than historical probabilities, eliminating information loss and significantly improving assessment reliability.
Solution Approach 2:
The system pre-deploys sensor networks and agent entities at potential event locations before incidents occur. These preliminary placements ensure that real-time data collection capabilities are already in place when events happen, eliminating the need for reactive information gathering and ensuring continuous reliable risk assessment data flow.
3Adaptability or versatility
If existing solutions are used, then risk management can be performed, but they cannot consider the natural evolution of events like fire or terrorist attacks
Solution Approach 1:
The system models event evolution dynamically by incorporating knowledge graphs that represent how different types of events naturally progress over time. As sensors detect current event characteristics, the system compares them against evolutionary models to predict future states, thereby considering natural event evolution while maintaining precise threat classification through continuous real-time data integration.
Solution Approach 2:
The system introduces knowledge graphs as intermediary structures that bridge current sensor data and future event predictions. These knowledge graphs encode domain expertise about how fires, terrorist attacks, and other events naturally evolve, allowing the system to consider event evolution trajectories while maintaining measurement precision through structured reasoning about potential future states.
Data Source
AI summary
The present invention discloses a real-time prevention and emergency management system and respective method of operation.The system comprises a Central Processing Unit (1), a plurality of Local Units (2), a plurality of Local Entities (3) and a plurality of Agent Entities (4), which are connected together by means of a communication network (5).The architecture of the system implements a stratified hierarchical scheme, where the elements are organized in 3-layers, being the Central Processing Unit (1) the core-knowledge and processing element. The Local Units (2), belong to a second layer, and are responsible for generating processed-data from event's raw data collected by the Agent Entities (4) and Local Entities (3), representing a third layer, that are assign to each Local Unit. The Agent Entities (4) can be of a singular or collective type, and are organized according to a hierarchical scheme.