Sensor Data Fusion Using Curation for Heterogeneous Sources
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing sensor data fusion systems fail to accurately fuse heterogeneous, partially heterogeneous, or homogeneous data sources, leading to inefficiencies in computational processing, storage demands, and lack of actionable data.
Innovation Solution
A system and method for sensor data fusion that uses a computer processor to curate, link, fuse, infer, and validate sensor data from multiple sources, creating a unique dataset with new data points and enhancing sensor accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor data from multiple sources is fused together, then the accuracy and actionable value of the data increases, but the computational processing requirements and storage demands increase
Solution Approach 1:
The patent applies preliminary action by curating and preprocessing sensor data before fusion operations. The system performs data curation, filtering, and validation in advance to reduce the volume and complexity of data requiring intensive computational processing during fusion, thereby lowering real-time energy consumption while maintaining fusion accuracy.
Solution Approach 2:
The patent extracts and removes redundant, invalid, or low-value data points from the sensor dataset before fusion. By taking out unnecessary computational overhead and storing only validated, actionable data in the data lake, the system reduces storage demands and computational requirements while preserving the essential information needed for accurate fusion.
2Adaptability or versatility
If heterogeneous sensor data sources are fused, then the comprehensiveness and actionable value of the dataset increases, but the complexity of data processing and validation increases
Solution Approach 1:
The patent implements universality through a unified data lake architecture that can accommodate heterogeneous sensor data sources with different formats, protocols, and characteristics. The system provides a common platform for storing, processing, and fusing diverse data types, enabling multi-functional data handling without requiring separate processing pipelines for each sensor type.
Solution Approach 2:
The patent uses an intermediary validation and curation layer between heterogeneous data sources and the fusion process. This intermediary component standardizes data formats, validates data quality, and transforms diverse sensor inputs into a unified structure, thereby reducing processing complexity while maintaining compatibility across different data sources.
3Loss of information
If all sensor data is stored and processed, then the completeness of the dataset increases, but the storage demands and processing time increase
Solution Approach 1:
The patent extracts and retains only the most valuable and actionable data points for long-term storage in the data lake. By identifying and removing redundant, duplicate, or low-information-value data, the system maintains data completeness for critical information while significantly reducing storage requirements for the overall dataset.
Solution Approach 2:
The patent implements a selective data retention strategy where low-value or redundant data is discarded from permanent storage, while essential data is preserved and can be recovered when needed. The system validates data quality and discards invalid or duplicate entries, recovering only the essential information needed for accurate fusion and analysis.
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.