Sensor Data Fusion Architecture for Low-Compute Predictive Linking
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Solution Overview
Problem
Existing sensor data fusion systems fail to create actionable data by correlating and fusing heterogeneous, partially heterogeneous, or homogeneous data sources efficiently, leading to excessive computational and storage requirements, and they do not generate new datasets that enhance sensor accuracy or predict future events.
Innovation Solution
A system and method for sensor data fusion that includes a computer processor with curation, link, fusion, inference, and validation engines, using artificial intelligence to mathematically link and validate sensor outputs exceeding a predefined threshold, creating a unique dataset.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor data from multiple sources is fused to create actionable data, then sensor accuracy and predictive capabilities are enhanced, but computational and storage requirements increase excessively
Solution Approach 1:
The patent segments the data fusion process into distinct functional engines (curation engine, link engine, fusion engine, inference engine, validation engine) that operate sequentially. Each engine processes specific aspects of data fusion independently, allowing computational tasks to be divided and managed more efficiently rather than processing all data simultaneously through a single complex system.
Solution Approach 2:
The curation engine performs preliminary actions by pre-processing and organizing sensor data before it reaches the fusion engine. This includes filtering, validating, and structuring data in advance, which reduces the computational burden on subsequent processing stages and prevents unnecessary calculations on invalid or irrelevant data.
2Adaptability or versatility
If heterogeneous sensor data sources are correlated and fused, then new datasets with predictive capabilities are generated, but system complexity increases
Solution Approach 1:
The patent implements a universal data fusion architecture where the same set of engines (curation, link, fusion, inference, validation) handles multiple types of sensor data sources. This multi-functional system can process heterogeneous data (sensor readings, images, videos, audio) through a common framework, reducing overall system complexity compared to having separate processing systems for each data type.
Solution Approach 2:
The link engine acts as an intermediary that establishes mathematical relationships between different sensor outputs. By introducing this intermediate component that specifically handles the correlation and linking of heterogeneous data sources, the system manages complexity in a localized manner rather than requiring the entire system to handle all data integration challenges simultaneously.
3Reliability
If mathematical validation is performed on sensor outputs exceeding a predefined threshold, then data reliability is improved, but processing time increases
Solution Approach 1:
The validation engine performs partial validation by applying mathematical validation only to sensor outputs that exceed a predefined threshold. This selective approach validates only the data points that require verification, rather than validating all data uniformly, thereby reducing overall processing time while maintaining reliability for critical measurements.
Solution Approach 2:
The system dynamically adjusts the predefined threshold parameter based on operational conditions and data characteristics. By changing this parameter, the system can optimize the balance between validation thoroughness and processing speed, allowing faster processing when thresholds are set to filter out obviously valid data, and more thorough validation when thresholds need to capture subtle anomalies.
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.