Emergency Management System with Risk-Based Sensor Prioritization
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Solution Overview
Problem
Existing emergency management systems rely on weather reports and ad-hoc distress signals, leading to inefficient and inconsistent response to local emergencies, as they lack the capability to automatically aggregate and analyze data from dispersed monitoring devices and sensors.
Innovation Solution
A system that alerts users of local emergencies and generates a graphical pathway to a safe zone by collecting and analyzing data from sensors, optimizing computational resources, and prioritizing data communication based on risk levels and network traffic.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If the system communicates with all subsystems uniformly to collect data from every property, then complete data coverage is achieved, but computational resource usage and network traffic increase significantly
Solution Approach 1:
The system applies different data collection strategies to different subregions based on their risk characteristics. High-risk subregions receive intensive monitoring with frequent data requests, while low-risk subregions receive minimal monitoring. This local differentiation allows complete data coverage where needed while conserving computational resources in areas where it is not critical.
Solution Approach 2:
The system performs partial data collection by focusing computational efforts only on subregions that exceed risk thresholds. Instead of uniformly collecting data from all properties, the system selectively intensifies data collection in high-risk areas while reducing or suspending it in low-risk areas, achieving effective monitoring with reduced resource consumption.
2Reliability
If the system collects data from all properties uniformly, then comprehensive emergency assessment is achieved, but response time and efficiency decrease due to processing large volumes of data
Solution Approach 1:
The geographic region is divided into multiple subregions, each assessed independently for emergency risk. This segmentation allows the system to process and evaluate each subregion separately, identifying and prioritizing high-risk areas without being burdened by uniform processing of all properties. The segmentation enables focused analysis that maintains assessment accuracy while improving response efficiency.
Solution Approach 2:
The system dynamically changes the parameter of data collection intensity based on risk assessment results. Subregions exceeding risk thresholds trigger intensified data collection and analysis, while subregions below thresholds receive reduced monitoring. This parameter adjustment allows the system to maintain reliable emergency assessment for at-risk areas while improving overall response efficiency by reducing processing load.
3Productivity
If the system prioritizes data collection from high-risk subregions, then response efficiency improves, but data coverage in low-risk subregions may be insufficient
Solution Approach 1:
The system implements periodic reassessment of subregion risk levels, dynamically adjusting data collection intensity. Low-risk subregions are monitored at reduced intensity but not completely neglected, with periodic checks to ensure conditions haven't changed. This periodic action maintains adequate data coverage across all areas while prioritizing resources for high-risk subregions, balancing response efficiency with comprehensive data coverage.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for an emergency management system. One of the methods includes receiving, for each of a plurality of properties in a geographic region and during an emergency in a first subregion of the geographic region having a first property, sensor data from sensors located at the respective property. The plurality of properties includes the first property and a second property. A risk score is generated for a second geographic region that indicates a likelihood that properties will be impacted by the emergency. In response, the method determines whether the risk score satisfies a score threshold, the method proceeds by determines a safe zone that indicates a physical location at which a risk of injury is reduced and presents a pathway on a map displayed on a user device to the safe zone.