Sensor Data Fusion with Pre-Storage Linking for Real-Time Processing
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Solution Overview
Problem
Existing sensor 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 data processing capabilities, especially for heterogeneous and partially heterogeneous data sources.
Innovation Solution
A system and method for sensor data fusion that includes a computer processor with curation, link, fusion, inference, and validation engines to curate and mathematically link sensor data, creating a unique dataset in near real-time, reducing computational and storage demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If sensor data is stored before fusion, then data availability is improved, but computational and storage requirements increase excessively
Solution Approach 1:
The system performs preliminary actions by curating and mathematically linking sensor data in near real-time before storage, using curation engines to organize data and link engines to establish mathematical relationships between data points from different sensors, thereby reducing the volume of data requiring storage while maintaining availability
Solution Approach 2:
The fusion engine extracts and creates new actionable data points by combining information from multiple sensor sources, storing only the essential fused results rather than all raw sensor data, which significantly reduces storage requirements while preserving critical information
2Device complexity
If sensor data fusion is performed after storage, then data processing is simplified, but real-time processing capabilities are lost
Solution Approach 1:
The system dynamically balances processing complexity and real-time capability by implementing multi-stage fusion: initial curating and linking in near real-time with simplified operations, followed by more complex fusion operations on the curated data, allowing real-time responsiveness while managing computational complexity
Solution Approach 2:
Preliminary curating and mathematical linking of sensor data is performed before fusion to organize and pre-process data in near real-time, reducing the complexity of subsequent fusion operations while maintaining real-time processing capabilities
3Productivity
If heterogeneous sensor data is fused without mathematical linking, then fusion speed is improved, but data accuracy decreases
Solution Approach 1:
The link engine performs preliminary mathematical linking of heterogeneous sensor data in near real-time, establishing quantitative relationships between different sensor measurements before fusion, which maintains data accuracy while enabling efficient subsequent fusion operations
Solution Approach 2:
Mathematical models and transformation functions act as intermediaries between heterogeneous sensor data sources, converting different data formats and units into a common framework that enables both accurate and efficient fusion
4Loss of information
If all sensor data is processed equally, then data completeness is improved, but power consumption increases
Solution Approach 1:
The curation engine applies local quality by differentiating data processing based on relevance, prioritizing curating and linking of high-value sensor data while reducing processing of redundant information, maintaining data completeness for critical sensors while reducing overall power consumption
Solution Approach 2:
The system performs partial processing by focusing computational resources on the most critical sensor data fusion operations in near real-time, rather than equally processing all sensor data, achieving adequate data completeness for key functions while significantly reducing power consumption
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.