Sensor Data Fusion with Curation and Linking for Predictive Accuracy
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing sensor data fusion systems fail to create actionable data by curating and linking sensor data before fusion, leading to excessive storage and computational requirements, and they do not generate new datasets that provide accuracy and predictive insights.
Innovation Solution
A system that includes a computer processor with curation, link, fusion, inference, and validation engines to mathematically link and fuse sensor data, creating a unique dataset that is validated and stored only when necessary, reducing computational and storage demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensor data is stored and processed without curation and linking, then storage and computational requirements remain excessive, but the system cannot create actionable data with predictive insights
Solution Approach 1:
The system performs curation and linking of sensor data before fusion occurs. The curation engine pre-processes raw sensor data to extract relevant features and remove redundancies, while the link engine establishes relationships between curated data points. This preliminary processing reduces the volume and complexity of data requiring storage and computation during fusion operations, thereby improving reliability without excessively increasing device complexity
Solution Approach 2:
The invention divides the data fusion system into distinct functional modules: a curation engine for pre-processing, a link engine for establishing relationships, and a fusion engine for combining data. This segmentation allows each component to perform its specific function efficiently, reducing the computational burden on any single element and enabling the system to create actionable data with predictive insights while managing complexity
2Loss of information
If all sensor data is fused and stored, then comprehensive datasets are available, but power consumption increases
Solution Approach 1:
The curation engine extracts only the most relevant and informative features from raw sensor data, discarding redundant and irrelevant information. This extraction process maintains the essential information needed for accurate fusion and predictive insights while significantly reducing the amount of data that requires processing and storage, thereby lowering power consumption without sacrificing data completeness
Solution Approach 2:
The system performs partial fusion by selectively combining only those curated and linked data points that are most relevant to specific fusion objectives, rather than fusing all available sensor data. This partial action approach maintains data completeness for critical information while reducing the computational workload and power consumption associated with processing excessive data
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.