AI Emergency Response Action Plan Generation
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Solution Overview
Problem
Emergency response departments face challenges in coordinating and planning effective responses to varying fire incidents due to the diverse causes and development patterns of fires.
Innovation Solution
A method and system for cognitive generation of an emergency response action plan using a processor that receives data on an emergency event, identifies features using an AI model, determines associated emergency response tasks, and generates an action plan that incorporates these tasks for output to users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual coordination and planning is used for fire incident responses, then flexibility and adaptability to varying situations is maintained, but response time and coordination efficiency deteriorate
Solution Approach 1:
The system performs preliminary actions by pre-establishing a knowledge base of fire incident patterns, causes, and effective response strategies. When an incident occurs, the AI model quickly matches the situation against pre-existing knowledge structures to generate coordinated response plans, eliminating the need for manual coordination from scratch and reducing response time.
Solution Approach 2:
The patent replaces the mechanical manual coordination process with an AI-based automated system. The AI model processes incident data, identifies patterns, and generates coordinated response plans automatically, substituting human manual coordination with intelligent automation that operates faster and consistently.
2Reliability
If standardized response protocols are implemented, then response consistency and reliability are improved, but adaptability to varying fire incident causes and development patterns deteriorates
Solution Approach 1:
The system implements dynamic response protocols that adapt to varying fire incident conditions. The AI model analyzes the specific characteristics of each incident (cause, development pattern, severity) and dynamically adjusts the response plan accordingly, rather than following a fixed standardized protocol. This maintains reliability through consistent AI-driven decision-making while achieving adaptability through real-time condition assessment.
Solution Approach 2:
The patent changes the parameters of response protocols based on incident characteristics. The AI model identifies key parameters such as fire cause, location, severity, and development pattern, then modifies the response strategy parameters accordingly. This allows the system to maintain reliable response consistency while adapting to varying incident types through parameter adjustment rather than rigid standardization.
3Measurement precision
If comprehensive data collection and analysis is performed for each emergency event, then response accuracy and effectiveness are improved, but system complexity and processing requirements deteriorate
Solution Approach 1:
The system employs a universal AI model that handles multiple functions: data collection, pattern recognition, cause analysis, and response plan generation. This multi-functional approach consolidates what would otherwise require multiple separate complex systems into a single integrated platform, reducing overall system complexity while maintaining high response accuracy through comprehensive data analysis.
Solution Approach 2:
The patent uses the AI model to create a virtual copy or representation of the fire incident scenario, allowing comprehensive analysis without physically implementing all possible response scenarios. The AI generates and evaluates multiple potential response plans based on copied incident characteristics, enabling accurate response selection without the complexity of testing all possibilities in real-world conditions.
Data Source
AI summary
A processor may receive data regarding an emergency event. The processor may identify, using an artificial intelligence model, one or more features regarding the emergency event. The processor may determine that the one or more features are associated with one or more emergency response tasks. The processor may generate an action plan that incorporates the one or more emergency response tasks. The processor may output the action plan to a user.