Dynamic Weighted Graph for Real-Time Emergency Route Retrieval
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Solution Overview
Problem
Existing systems fail to update weighted graphs in real time to account for changing conditions such as fire spread, evacuee movement, and door closures, which are crucial for optimal route retrieval in dynamic environments like buildings during emergencies.
Innovation Solution
A method and system that automatically generates a weighted graph from a building's blueprint, updates it in real time using sensor and predictive data by modifying edge weights and splitting/merging edges, and computes an optimal route using algorithms like Dijkstra's for dynamic environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a weighted graph is generated from a building blueprint for route retrieval, then optimal routes can be computed, but the system cannot account for changing conditions such as fire spread, evacuee movement, and door closures
Solution Approach 1:
The patent applies dynamics by transforming the static weighted graph into a dynamic structure that automatically updates in real-time. The system continuously modifies edge weights based on changing conditions (fire spread, smoke density, door closures) and dynamically adds/removes nodes and edges as the building state evolves, enabling the graph to adapt to real-time emergencies while maintaining route accuracy
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring sensor data (temperature, smoke density, door status) and using this information to update the weighted graph. The feedback loop processes real-time building conditions, recalculates edge weights, and recomputes optimal routes, ensuring the system responds accurately to changing emergency conditions
2Adaptability or versatility
If the weighted graph is updated in real time using sensor data, then the system can adapt to changing conditions, but the computational complexity and processing time increase
Solution Approach 1:
The patent applies segmentation by dividing the building into discrete zones with specific nodes and edges in the weighted graph. Each zone can be independently updated based on local sensor data, allowing partial graph updates rather than complete recalculations. This segmentation reduces computational complexity by focusing processing only on affected areas while maintaining real-time adaptability
Solution Approach 2:
The system uses parameter changes by modifying edge weights based on sensor readings (temperature, smoke density, door status) rather than restructuring the entire graph. This approach changes numerical parameters of existing graph elements dynamically, enabling real-time adaptation with reduced computational overhead compared to complete graph reconstruction
3Measurement precision
If multiple sensors and predictive data sources are integrated, then the system can detect changing conditions accurately, but the system complexity and data processing requirements increase
Solution Approach 1:
The patent applies universality by designing a unified weighted graph framework that can process multiple types of sensor data (temperature, smoke density, door status, evacuee movement) through a single consistent mechanism. The same graph structure and update algorithms handle diverse data sources, reducing system complexity by providing a universal interface for multi-functional data integration
Data Source
AI summary
A method of route retrieval is provided comprising initializing a first weighted graph, converting a blueprint of an area into a weighted graph, updating the weighted graph in real time, and calculating an optimal route in the area.