Industrial IoT Gateway Data Modeling for Faster AI Insights
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Solution Overview
Problem
Industrial automation systems face challenges in deriving value from large amounts of unstructured and uncorrelated industrial data, leading to inefficiencies in data processing and storage, and often produce spurious correlations that require human verification, resulting in wasted resources and prolonged time-to-value for insights.
Innovation Solution
A smart gateway platform that leverages domain expertise to selectively model and structure relevant industrial data, using a processor to customize model templates, normalize data, and send structured data to AI analytics systems, reducing the data space and applying pre-defined correlations to quickly derive actionable insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all industrial data is collected and stored for analysis, then the completeness of data is improved, but the data storage and processing requirements increase significantly
Solution Approach 1:
The patent extracts only the relevant subset of industrial data needed for specific analytics objectives rather than collecting all available data. The system identifies and extracts relevant data points based on pre-defined analytics models, thereby reducing storage requirements while maintaining analytical completeness.
Solution Approach 2:
The patent segments industrial data into relevant and irrelevant portions based on analytics objectives. By dividing the data space into meaningful segments and only processing relevant segments, the system reduces overall data storage requirements while preserving necessary information for analysis.
2Loss of information
If all industrial data is processed for analytics, then the comprehensiveness of insights is improved, but the processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by pre-defining analytics models and identifying relevant data relationships before actual data processing. This preliminary structuring of data and analytics frameworks enables faster processing when insights are needed, reducing time-to-value while maintaining comprehensive analysis capabilities.
Solution Approach 2:
The patent extracts only the essential data relationships and features needed for specific analytics objectives, rather than processing all industrial data. This selective extraction significantly reduces computational workload and processing time while preserving the comprehensiveness of insights for targeted analytics goals.
3Quantity of substance
If comprehensive data analysis is performed without domain expertise guidance, then the coverage of analysis is improved, but the accuracy of insights decreases due to spurious correlations
Solution Approach 1:
The patent introduces domain expertise as an intermediary between raw industrial data and analytics insights. Pre-defined analytics models incorporating domain knowledge act as mediators that guide data analysis, filtering out spurious correlations while maintaining broad data coverage. This intermediary layer ensures that comprehensive data analysis produces accurate, actionable insights.
4Loss of information
If extensive data collection and processing is performed, then the potential value of insights is improved, but the resources required for verification and validation increase
Solution Approach 1:
The patent extracts only the most valuable and relevant data subsets for analysis based on pre-defined analytics models and domain expertise. By focusing computational and verification resources on high-value insights rather than all collected data, the system maximizes insight value while minimizing the resources required for verification and validation.
Data Source
AI summary
A smart gateway platform leverages pre-defined industrial expertise to identify limited subsets of available industrial data deemed relevant to a desired business objective, and to collect and model this relevant data to apply useful constraints on subsequent artificial intelligence or machine learning analytics applied to the data. This approach can reduce the data space to which AI analytics are applied, and assist data analytic systems to more quickly derive valuable insights and business outcomes. In some embodiments, the smart gateway platform can operate within the context of a multi-level industrial analytic system, feeding pre-modeled data to one or more AI or machine learning systems executing on one or more different levels of an industrial enterprise.