AI Emergency Mapping for Real-Time Dispatch Incident Updates
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Solution Overview
Problem
Existing technologies have not effectively addressed the challenges of integrating machine learning solutions for enhancing emergency mapping and data management within the context of emergency services, particularly in the field of emergency response and data management systems, such as emergency response networks, to provide real-time, accurate, and efficient emergency data management and dispatching.
Innovation Solution
Implementing a cloud-based emergency data management system using artificial intelligence, specifically focusing on emergency data management systems, to enhance emergency mapping and computer-aided-dispatch incident creation and updates, utilizing machine learning to convert emergency data into usable formats for emergency communication centers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning solutions are integrated into emergency mapping and data management systems, then operational efficiency and accuracy are improved, but system complexity increases
Solution Approach 1:
The patent introduces a cloud-based emergency data management system as an intermediary layer between legacy emergency communication systems and modern machine learning capabilities. This intermediary system handles data conversion, processing, and mapping functions, allowing machine learning models to operate without directly complicating the core emergency response infrastructure. The cloud-based platform acts as a buffer that manages complexity while delivering improved operational efficiency.
2Measurement precision
If machine learning models are deployed for emergency data processing, then data management accuracy improves, but implementation difficulty increases
Solution Approach 1:
The patent segments the emergency data management system into distinct functional modules: legacy emergency communication systems, a cloud-based data management platform, and machine learning processing components. This segmentation allows each component to be developed, tested, and deployed independently. The machine learning models process data in isolated modules rather than requiring complete system redesign, thereby improving accuracy while reducing implementation difficulty through modular architecture.
3Adaptability or versatility
If legacy emergency communication systems are modernized with new components, then system capabilities improve, but integration complexity increases
Solution Approach 1:
The cloud-based emergency data management system serves as an intermediary that connects legacy emergency communication systems with modern machine learning capabilities. It provides standardized interfaces for data exchange, handles protocol conversions, and manages the integration between heterogeneous systems. This intermediary layer enables enhanced system capabilities while isolating the complexity of integration from the core emergency response functions.
Data Source
AI summary
A disclosed method implements training an artificial intelligence module in a cloud-based emergency data management system using training data comprising emergency call data from an emergency communication center (ECC); generating a script, in response to the training data, by the artificial intelligence module, the script for converting emergency call data into a mapping subprocess format of the cloud-based emergency data management system; and configuring a cloud-based processor using the script.