Geospatial Asset Data Pruning and Image Processing
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Solution Overview
Problem
Current asset management systems for infrastructure and assets face challenges in efficiently collecting and processing data, leading to outdated information, misreporting, and limited data integrity, which can result in missed or improperly assessed asset issues.
Innovation Solution
A system comprising data collection devices with cameras and sensors, servers, and client interfaces, utilizing image processing, geospatial functions, artificial intelligence, and data pruning algorithms to selectively capture, process, and store asset data, enabling real-time updates and maintenance issue identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection methods are used to collect asset data, then implementation cost is low, but data accuracy and completeness deteriorate due to human error and inefficiency
Solution Approach 1:
The patent replaces manual mechanical inspection processes with automated electronic systems including cameras, sensors, and image processing algorithms. The system automatically captures asset images, extracts features through computer vision, and updates asset records without human intervention, thereby improving data accuracy while managing system complexity through modular architecture.
Solution Approach 2:
The system enables self-service asset monitoring by automatically detecting asset conditions, identifying issues, and generating maintenance alerts without requiring manual inspection. The automated image processing and asset matching systems continuously monitor assets and self-correct data inconsistencies, reducing reliance on human inspectors.
2Loss of information
If comprehensive data collection is performed on all assets, then data completeness improves, but processing time and computational resources worsen
Solution Approach 1:
The patent extracts and processes only relevant asset data and features from collected images using image processing algorithms. The system identifies and extracts key asset characteristics, condition indicators, and change detections while discarding irrelevant information, thereby maintaining data completeness for critical parameters while reducing overall processing time.
Solution Approach 2:
The system performs partial processing by focusing computational resources on assets showing changes or anomalies rather than processing all assets uniformly. The image processing pipeline applies different levels of analysis based on asset priority and detected changes, reducing total processing time while maintaining completeness for critical assets.
3Reliability
If frequent asset inspections are conducted to maintain up-to-date records, then asset condition monitoring improves, but operational costs and resource consumption worsen
Solution Approach 1:
The patent implements periodic automated image capture and processing cycles at optimized intervals based on asset criticality and historical change patterns. The system schedules inspections dynamically, performing more frequent checks on high-priority assets and less frequent checks on stable assets, thereby improving monitoring reliability while optimizing resource utilization and operational efficiency.
Data Source
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AI summary
The present invention is intended to provide enhanced asset data collection functions, asset analysis, and/or asset alerts. The system assists in updating existing asset records, adding new assets to asset inventories, identifying maintenance issues, and/or identifying assets which are no longer present. The system is composed of a data collection device(s) which can collect images and location sensor data, server(s), and client interface(s) for interacting with the collected and/or processed data. The system also includes image processing operations and data pruning and selection functions for the smart asset data collection and processing, allowing to obtain the appropriate data for the appropriate asset at the appropriate time.