Sensor Data Fusion Using Pre-Storage Correlation
Find Innovative SolutionsGenerate Solutions
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.
Innovation Solution
A system and method for sensor data fusion that includes capturing, curating, linking, fusing, inferring, and validating sensor data in real-time or near real-time, creating a unique dataset by correlating data before storage, thereby reducing computational and storage demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If sensor data is fused after storage in existing systems, then data fusion can be performed, but computational and storage requirements become excessive
Solution Approach 1:
The patent applies preliminary action by curating and linking sensor data before storage. The system performs data curation, linking, and fusion operations in advance, organizing data into correlated groups that can be stored more efficiently. This pre-processing reduces the volume of data that needs to be stored while maintaining the capability to perform comprehensive data fusion when needed.
Solution Approach 2:
The patent segments sensor data into correlated groups or clusters based on relationships between different sensor types. By dividing the overall dataset into meaningful segments that are already linked and curated, the system reduces storage requirements while preserving the ability to perform comprehensive analysis. Each segment contains pre-processed, inter-related data that can be stored more compactly.
2Productivity
If sensor data is fused after storage in existing systems, then data fusion can be performed, but computational requirements become excessive
Solution Approach 1:
The system performs data curation, linking, and preliminary fusion operations before storage, reducing the computational burden during actual data fusion tasks. By pre-processing the data and establishing relationships in advance, the system minimizes the computational energy required when performing comprehensive data fusion, as much of the heavy lifting has already been completed.
Solution Approach 2:
The system automatically curates and links sensor data without requiring intensive post-storage processing. The automated data linking and curation processes reduce the need for energy-intensive computational operations later, as the system serves itself by maintaining organized, pre-processed data structures that require minimal computational resources to access and fuse.
3Quantity of substance
If sensor data is not correlated before storage, then storage is simpler, but actionable data cannot be created
Solution Approach 1:
The system performs preliminary data correlation and linking before storage, creating actionable insights while maintaining storage efficiency. By curating and linking data in advance, the system preserves the relationships and contextual information needed to generate actionable data, while storing only the essential correlated information rather than raw, unprocessed datasets.
Solution Approach 2:
The system extracts and stores only the correlated relationships and essential data elements needed to create actionable insights, rather than storing complete raw datasets. By taking out and preserving only the critical linked information, the system maintains storage simplicity while ensuring that actionable data can be generated from the stored correlated relationships.
4Productivity
If real-time sensor data fusion is implemented, then actionable data is provided, but power consumption increases
Solution Approach 1:
The system performs preliminary data curation, linking, and organization in advance, reducing the computational power needed for real-time fusion operations. By having data pre-prepared and correlated before storage, the system can perform real-time fusion with minimal processing power, as the heavy computational tasks have already been completed during the preliminary data preparation phase.
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.