Atmospheric Corrosivity Mapping via Geospatial Data Aggregation
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Solution Overview
Problem
Current methods lack effective tools for prioritizing and predicting atmospheric corrosion, particularly in outdoor environments, leading to significant economic losses due to the deterioration of metals and other materials exposed to corrosive atmospheric conditions.
Innovation Solution
A system that aggregates disparate datasets to generate atmospheric corrosivity maps by combining geospatial locations with aspatial parameters such as atmospheric pollution, salinity, and moisture, using geo-statistical techniques and modeling to create a grid representing corrosivity levels, which can be overlaid on geographic maps to identify high, medium, and low corrosion risk areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If atmospheric corrosion monitoring and assessment tools are improved, then corrosion risk prediction accuracy is improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system segments the assessment process into distinct functional modules: data acquisition module that collects environmental parameters, data processing module that aggregates and analyzes the parameters, and output module that generates corrosivity maps. This segmentation allows each module to be optimized independently while maintaining overall system accuracy.
Solution Approach 2:
The patent introduces an intermediary processing layer that aggregates multiple environmental parameters (pollution, salinity, moisture) and transforms them into a unified corrosivity assessment. This intermediary layer simplifies the complexity by providing a standardized interface between diverse data sources and the final assessment output.
2Measurement precision
If comprehensive datasets are aggregated to improve corrosivity assessment, then prediction accuracy is improved, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-aggregating environmental parameter data into structured datasets before full analysis. Data from multiple sources is collected and organized in advance, allowing the main corrosivity assessment to process pre-prepared information rather than raw data, significantly reducing processing time.
Solution Approach 2:
The patent transforms multiple environmental parameters (pollution levels, salinity, moisture) into a standardized corrosivity scale parameter. This parameter transformation consolidates diverse data into a unified metric that can be processed more efficiently while maintaining comprehensive assessment accuracy.
3Reliability
If detailed corrosivity mapping is implemented, then corrosion risk identification is improved, but implementation cost and resource requirements increase
Solution Approach 1:
The system is designed with universal functionality that can assess corrosivity across multiple locations and applications using the same platform. The standardized corrosivity scale and mapping approach can be applied to different geographic areas and asset types without requiring separate systems, reducing per-deployment costs while maintaining reliable identification.
Data Source
AI summary
A plurality of disparate datasets is aggregated into a geodata data structure specifying a plurality of geospatial locations and a set of aspatial parameters at each geospatial location. Each aspatial parameter is combined at each geospatial location to generate an atmospheric corrosivity scale parameter at each of the plurality of geospatial locations. A grid is created with cells representing each of the plurality of geospatial locations and each of the corresponding atmospheric corrosivity scale parameters. The grid is stored for output of at least a portion of the plurality of geospatial locations and the corresponding atmospheric corrosivity scale parameters overlaid on a geographic map.