Network Recovery Sequence Generation via Scientific Engine Analysis
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Solution Overview
Problem
Current methods lack practical tools for recovering complex networks from disruptions, particularly when component-specific information is unknown, and existing studies have not provided effective methods for restoring networks to partial or complete functionality after hazards.
Innovation Solution
A method and system that utilize a backend computing device with a scientific engine to determine a priority recovery sequence for networks by analyzing node and link data, user preferences, and hazard-specific metrics, allowing for the restoration of networks from disruption to desired states of functionality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If practical recovery tools are developed for disrupted networks, then network recovery effectiveness is improved, but system complexity increases
Solution Approach 1:
The system segments the network recovery problem into discrete components: nodes represent individual network elements, links represent connections, and the scientific engine processes these segmented elements to generate recovery sequences. This segmentation allows complex network recovery to be broken down into manageable analytical units.
Solution Approach 2:
The scientific engine acts as an intermediary between the disrupted network state and the recovery strategies. It receives network data, hazard information, and user preferences as input, processes this information through computational algorithms, and outputs optimized recovery sequences that restore network functionality.
2Measurement precision
If component-specific information is required for recovery, then recovery precision is improved, but information availability decreases
Solution Approach 1:
The system changes parameters from requiring detailed component-specific information to using aggregate network-level data. The scientific engine operates with node and link attributes, hazard types, and user preferences rather than requiring granular component details, thereby maintaining recovery precision while reducing information requirements.
Solution Approach 2:
The recovery system is designed to be universal and applicable to different network types without requiring network-specific customization. The scientific engine handles various network configurations using the same fundamental approach, making the system broadly applicable while maintaining effectiveness across different contexts.
3Productivity
If multiple restoration strategies are generated and compared, then recovery optimization is improved, but computational time increases
Solution Approach 1:
The system generates multiple recovery strategies beyond what a single approach would provide, allowing comparison and selection of optimal sequences. By producing several candidate recovery paths, the system ensures optimized recovery while the computational overhead is managed through efficient algorithmic processing.
Solution Approach 2:
The system incorporates user feedback through the user interface, allowing users to input preferences, constraints, and priorities. This feedback loop enables thescientific engine to refine and adjust recovery sequences based on user requirements, optimizing recovery strategies while accounting for real-world operational constraints.
Data Source
AI summary
A method and system for providing a recovery sequence for a network from a state of disruption to a state of partial or complete functionality are provided. The method and system can measure the response of a network to one or multiple hazards and can generate and compare the effectiveness of multiple recovery strategies in a quantitative and generalizable manner, providing a user with practical tools to implement an efficient restoration of the network.