Real-Time Sensor Data Fusion With Pre-Storage Correlation
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Solution Overview
Problem
Existing data fusion systems fail to create actionable data by correlating and fusing sensor data before storage, leading to excessive computational and storage requirements, and lack real-time accuracy assessment.
Innovation Solution
A system and method for sensor data fusion that includes a computer processor to curate, link, fuse, infer, and validate sensor data in real-time, creating a new dataset by correlating data before storage, thereby reducing computational and storage demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor data is fused after storage in existing systems, then data availability is maintained, but computational requirements and storage demands become excessive
Solution Approach 1:
The system performs data curation, linking, and fusion operations before final storage, preparing data in advance for future queries. This preliminary action reduces the computational burden during actual query execution, as the data is already organized and fused when needed.
Solution Approach 2:
The patent divides the data processing workflow into distinct sequential stages: curation engine for data preparation, link engine for establishing relationships, fusion engine for combining data, inference engine for deriving insights, and validation engine for quality assurance. This segmentation allows each component to specialize and operate efficiently.
2Loss of information
If sensor data is fused after storage in existing systems, then complete data sets are available, but storage requirements become excessive
Solution Approach 1:
The system performs data fusion before storage, creating consolidated datasets that contain only the essential fused information rather than storing all raw sensor data. This preliminary fusion action reduces storage requirements while maintaining data completeness for the fused results.
Solution Approach 2:
The system extracts only the necessary fused data results after processing, rather than storing all intermediate and raw data. The curation, linking, and fusion engines extract essential information and store only what is needed, reducing storage demands while preserving data completeness.
3Reliability
If sensor data fusion is performed in existing systems, then data integration is achieved, but real-time accuracy assessment is lacking
Solution Approach 1:
The validation engine provides feedback on the quality and accuracy of fused data by comparing results against expected patterns and using probabilistic context. This feedback mechanism enables real-time accuracy assessment, allowing the system to identify and correct issues in data integration.
Solution Approach 2:
The system performs self-validation of its own data fusion processes through the validation engine, which automatically assesses accuracy without external intervention. This self-service capability enables real-time monitoring and assessment of data integration quality.
4Productivity
If multiple engines are used for sensor data processing, then processing capability is enhanced, but system complexity increases
Solution Approach 1:
The system divides complex data processing into specialized engines: curation, linking, fusion, inference, and validation. Each engine handles a specific aspect of processing, improving overall productivity through specialization while managing complexity through modular design.
Solution Approach 2:
Each engine is designed to be multi-functional within its domain, handling various types of sensor data and processing operations. This universality allows the system to process diverse data types through the same engine architecture, enhancing productivity without proportionally increasing complexity.
Data Source
AI summary
Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.