Intelligent Activation System for Emergency Halt Mechanisms
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Solution Overview
Problem
In disaster or cyber scenarios, existing systems lack an efficient mechanism for automatically activating a protected emergency system halt mechanism across various locations and network conditions, such as congested mobile data networks or locked-down remote connections, while ensuring security and reliability.
Innovation Solution
An intelligent activation system utilizing artificial intelligence (AI) and machine learning (ML) models to detect scenarios from multiple data sources, automatically activating or deactivating a protected emergency system halt mechanism, with voice and secondary authentication for authorized control, ensuring system halt or restart actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If automatic activation of emergency system halt mechanism is implemented, then response time and productivity are improved, but device complexity and reliability requirements increase
Solution Approach 1:
The system pre-configures AI/ML models and rules for scenario detection before emergencies occur. When a disaster or cyber scenario is detected, the pre-established activation logic automatically triggers the halt mechanism without requiring real-time manual decision-making, thus reducing response time while managing complexity through pre-planned workflows.
Solution Approach 2:
An intelligent activation system serves as an intermediary layer between monitoring systems and the emergency halt mechanism. This intermediary analyzes data from multiple sources using AI/ML models and rules, making informed decisions about activation while isolating the complexity of decision-making from the simple activation trigger, thereby reducing overall system complexity.
2Device complexity
If manual activation only is used, then device complexity is reduced, but loss of time and productivity decrease due to manual intervention requirements
Solution Approach 1:
The system enables self-service activation by automatically detecting disaster or cyber scenarios and triggering the emergency halt mechanism without requiring manual intervention. The AI/ML models continuously monitor data sources and autonomously make activation decisions based on pre-defined rules, eliminating delays associated with manual response while maintaining manageable complexity through automated decision logic.
3Reliability
If authentication requirements are increased, then reliability is improved, but ease of operation decreases during emergency situations
Solution Approach 1:
The system pre-establishes authentication credentials and authorization rules before emergencies occur. During critical situations, the system automatically verifies pre-configured authentication parameters and activates the halt mechanism without requiring real-time authentication challenges, thus maintaining high security while ensuring rapid response. The authentication burden is shifted to pre-configured credentials rather than real-time verification.
Data Source
AI summary
Various methods, apparatuses/systems, and media for implementing an emergency system halt mechanism are disclosed. A processor establishes a communication link with a plurality of data sources and a receiver. The receiver receives data related to disaster and/or cyber scenarios from the plurality of data sources. The processor automatically determines, by utilizing an intelligent activation system (IAS), whether to activate an emergency system halt mechanism (ESHM) upon detection of a disaster and/or a cyber scenario based on analyzing the received data and using pre-determined rules and artificial intelligence (AI)/machine learning (ML) models. The processor also automatically determines whether to activate or deactivate the ESHM based on a result of determination of the IAS. Once activated, a secure voice-based authentication processes designed to work in the limitations of cyber and disaster scenarios allow registered users to take actions such as halt or resume systems.