Physical Infrastructure Modeling with Confidence-Aware Aggregation
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Solution Overview
Problem
Utility service providers face challenges in obtaining comprehensive and accurate information about their physical infrastructure, which is essential for infrastructure sharing and efficient network operations, due to incomplete and inaccurate data from various sources.
Innovation Solution
A computer-implemented method to model physical infrastructure by aggregating and refining data from multiple sources, using rules to infer missing information and adjust confidence levels, enabling the generation of a reliable model for infrastructure components and their locations, and facilitating the deployment of new components and software-defined networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data from multiple sources is aggregated to improve comprehensiveness, then the quantity of infrastructure information increases, but data accuracy and reliability deteriorate due to inconsistencies and varying confidence levels
Solution Approach 1:
The patent changes the parameter of data confidence by introducing a quantitative confidence score for each data source and each record. This allows the system to weigh different data sources differently and track how confidence levels change through the aggregation and refinement process, resolving the contradiction between quantity and reliability.
Solution Approach 2:
The patent implements feedback mechanisms where the model is continuously refined based on rule satisfaction. The system evaluates whether aggregated data satisfies infrastructure rules and uses this feedback to adjust confidence levels and refine the model, ensuring that increased data quantity does not compromise reliability.
2Loss of information
If comprehensive infrastructure data is collected from multiple sources, then information completeness improves, but data processing complexity increases
Solution Approach 1:
The patent segments the data processing into distinct modules: data access from multiple sources, record aggregation by location and type, confidence level calculation, rule-based refinement, and model generation. This segmentation manages complexity by breaking down the comprehensive data processing task into manageable, independent steps.
Solution Approach 2:
The patent introduces an intermediary refinement process that sits between raw data aggregation and final model generation. This intermediary layer applies infrastructure rules to validate and adjust aggregated data, serving as a mediator that simplifies the transition from comprehensive but messy source data to a refined, reliable model.
3Measurement precision
If infrastructure rules are applied to refine the model, then data accuracy improves, but processing time increases
Solution Approach 1:
The patent performs preliminary aggregation and confidence level assignment before applying infrastructure rules. By pre-processing the data to organize it by location and type and assign initial confidence levels, the system reduces the computational burden during the rule-based refinement phase, thereby reducing overall processing time while maintaining accuracy improvements.
Data Source
AI summary
A computer implemented method to model physical infrastructure of a transmission network for a utility service, the physical infrastructure including a set of physical components in the network, including accessing each of a plurality of physical infrastructure data sources, each data source including records each storing information on at least a subset of the set of physical components including a location and type of each physical component in the subset, wherein each record has associated an indication of a degree of confidence of an accuracy of the record; generating a model of the physical infrastructure including an indication of a location and type of physical components based on the data sources, wherein records of the data sources having common location and type are aggregated for indication in the model; associating each indication in the model with a degree of confidence of accuracy of the indication based on the degree of confidence information from the data sources; accessing a set of rules defining relationships between types of physical component; and refining the model based on the rules including adjusting a degree of confidence of accuracy of indications in the model based on satisfaction of the rules.