Geographic Representation Points for Dynamic Risk Modeling
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Solution Overview
Problem
Existing methods for resource allocation and risk modeling in geographically distributed assets, such as rail systems, struggle with accurately characterizing risk exposure due to the lack of conventional addresses for continuous geographic distributions, leading to poor asset allocation and modeling results, especially for moveable assets and those with variable risk factors.
Innovation Solution
The use of geographic representation points and variable resolution grids in conjunction with meta-data to quantify risk exposure values by overlaying asset maps with baseline maps, allowing for precise identification of risk intersections and flexible geospatial representations, enabling accurate risk assessment and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional address-based methods are used for resource allocation, then implementation is simple, but accuracy of risk exposure characterization is poor for continuous geographic distributions
Solution Approach 1:
The patent segments continuous geographic distributions into discrete geographic representation points (GRPs) that can be individually addressed and analyzed. Each GRP represents a specific location along a continuous asset (such as a pipeline or power line) and can be independently tagged with risk factors, enabling precise risk exposure characterization while maintaining manageable data structures.
Solution Approach 2:
The patent introduces geographic representation points as intermediary elements between the continuous geographic asset and the discrete address-based risk modeling system. These GRPs serve as mediators that bridge the gap between continuous spatial distributions and conventional address-based methodologies, enabling accurate risk exposure assessment without requiring conventional street addresses for every location.
2Adaptability or versatility
If static resource allocation is used, then planning is straightforward, but adaptability to dynamic risk factors is poor
Solution Approach 1:
The patent enables dynamic resource allocation by allowing risk factors and resource requirements to be updated in real-time for each geographic representation point. As risks change (such as new hazards appearing or existing risks evolving), the system can dynamically adjust resource allocation decisions based on current risk exposure levels at each GRP, rather than relying on static historical data.
Solution Approach 2:
The patent utilizes parameter changes in risk exposure values to drive adaptive resource allocation. By continuously monitoring and updating risk parameters for each geographic representation point, the system can adjust resource distribution in response to changing conditions, such as seasonal variations, emerging hazards, or changes in asset vulnerability.
3Reliability
If uniform resource distribution is used, then allocation is simple, but optimization for variable risk factors is poor
Solution Approach 1:
The patent applies local quality by allowing different resource allocation strategies to be applied to different geographic representation points based on their specific risk characteristics. Each GRP can have customized resource requirements and risk factors tailored to its local conditions, enabling optimized resource distribution that reflects the varying risk landscape across the continuous asset rather than applying a one-size-fits-all approach.
Data Source
AI summary
A risk exposure model is developed for network or moveable assets not specific to a single, fixed address or location. An asset map using a plurality of geographic representation points is used to identify the physical locations of the asset portions (or possible physical locations in the case of a moveable asset). Baseline geographic, geologic, political, and demographic data is similarly represented using geographic representation points. Meta-data associated with each geographic representation point is used to identify details related to the asset or baseline feature at that geographic location. Risk exposure values are then calculated using the geographic representation points specific to the asset portions that are subject to risks associated with the location of the asset portion.