Sensor Data Fusion with Curation and Linking for Real-Time Inference
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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 and method for sensor data fusion that includes a computer processor with curation, link, fusion, inference, and validation engines to mathematically link and fuse heterogeneous, partially heterogeneous, or homogeneous data points, creating a unique dataset with accuracy values, reducing computational and storage demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensor data is stored and processed without curation and linking before fusion, then all raw data is available for analysis, but storage and computational requirements become excessive
Solution Approach 1:
The system performs preliminary curation and linking of sensor data before fusion occurs. The curation engine pre-processes incoming sensor streams to identify and retain only relevant data points based on contextual relationships, while the link engine establishes associations between curated data points. This preliminary processing ensures that when fusion occurs, only necessary data is combined, maintaining reliability while reducing storage and computational demands.
2Productivity
If heterogeneous sensor data points are fused without mathematical linking, then fusion can occur more freely, but accuracy and validation of the fused data decrease
Solution Approach 1:
The system implements feedback through the validation engine, which continuously assesses the quality and accuracy of fused data by examining the mathematical links established between data points. The validation engine provides feedback on fusion accuracy based on the strength and relevance of connections between heterogeneous sensor data, enabling the system to maintain measurement precision while preserving fusion productivity.
3Loss of information
If all sensor data is processed in real-time without selective curation, then no data is lost, but computational requirements and processing time increase significantly
Solution Approach 1:
The curation engine extracts and retains only the most relevant sensor data points based on their contextual relationships and potential contribution to meaningful insights. By taking out and keeping only essential data while discarding redundant information, the system maintains data completeness for critical information while dramatically reducing processing time and computational requirements.
4Device complexity
If sensor data is fused without creating new datasets, then processing is simpler, but actionable data with predictive insights cannot be generated
Solution Approach 1:
The fusion engine merges linked and curated sensor data points to create new synthesized datasets that contain actionable information and predictive insights. By combining multiple heterogeneous data sources that have been pre-linked through contextual relationships, the system generates new information that neither individual sensor could provide alone, enabling predictive capabilities while managing processing complexity through the preliminary curation and linking steps.
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.