Neural-Network Emergency Planning for Real-Time Scenario Detection
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Solution Overview
Problem
Traditional event tree/fault tree methodologies face challenges in modeling interactions among hardware, process, software, and human behavior, which hinders accurate identification of scenarios likely to lead to catastrophic events, particularly in nuclear facilities, and thus inadequate support for declaring site emergencies and emergency responses.
Innovation Solution
The implementation of a computer-implemented method using a neural network that processes real-time data through dynamic event trees, clustering, and deep learning to identify scenarios with similar behavior characteristics, projecting radiological outcomes, and guiding emergency responses by filtering and classifying scenarios based on observable parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional event tree/fault tree methodologies are used to model emergency scenarios, then the modeling process is simple and straightforward, but the ability to accurately identify scenarios likely to lead to catastrophic events is insufficient
Solution Approach 1:
The patent replaces traditional mechanical event tree/fault tree methodologies with a neural network-based deep learning system. The neural network processes real-time data from multiple sources (sensor data, process control data, human behavior data) to automatically identify scenarios likely to lead to catastrophic events, achieving higher accuracy without manual modeling complexity
Solution Approach 2:
The patent introduces a neural network as an intermediary between raw data and scenario identification. This intermediary processes and integrates multiple data sources (hardware, process, software, human behavior) to produce accurate scenario predictions, resolving the contradiction between simple input methods and complex analysis requirements
2Reliability
If real-time data processing with neural networks is implemented, then the accuracy of predicting undesirable outcomes is improved, but the computational complexity and data processing requirements increase
Solution Approach 1:
The patent segments the data processing system into distinct modules: data collection from multiple sources, preprocessing of different data types, neural network processing, and scenario identification. This segmentation manages complexity by organizing the system into manageable, specialized components while maintaining high reliability through comprehensive data analysis
Solution Approach 2:
The neural network is designed as a universal system that can process multiple types of data (sensor data, process control data, human behavior data) and identify various scenarios across different emergency types. This multi-functionality achieves high reliability without proportionally increasing system complexity through reusable processing architecture
3Adaptability or versatility
If comprehensive scenario analysis is performed to identify all potential emergency scenarios, then the completeness of emergency preparedness is improved, but the time required for analysis and response increases
Solution Approach 1:
The patent performs preliminary training of the neural network using historical scenario data before actual emergency situations occur. This preliminary action prepares the system to quickly identify scenarios in real-time without performing exhaustive analysis during emergencies, thus achieving comprehensive scenario coverage without time loss during critical moments
Solution Approach 2:
The system continuously processes real-time data and updates scenario identification without interruption. The neural network maintains continuous operation, analyzing data streams and identifying scenarios as they develop, ensuring complete scenario coverage while minimizing analysis time through uninterrupted processing
Data Source
AI summary
Systems and methods are described herein for real-time data processing and for emergency planning. Scenario test data may be collected in real-time based on monitoring local or regional data to ascertain any anomaly phenomenon that may indicate an imminent danger or of concern. A computer-implemented method may include filtering a plurality of different test scenarios to identify a sub-set of test scenarios from the plurality of different test scenarios that may have similar behavior characteristics. A sub-set of test scenarios is provided to a trained neural network to identify one or more sub-set of test scenarios. The one or more identified sub-set of test scenarios may correspond to one or more anomaly test scenarios from the sub-set of test scenarios that is most likely to lead to an undesirable outcome. The neural network may be one of: a conventional neural network and a modular neural network.