Edge Sensor Correlation via AI Object Parameter Matching
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Solution Overview
Problem
Conventional systems and methods are not capable of efficiently or accurately correlating sensor data from different edge devices deployed in dispersed locations, such as on earth or in space, which hinders the accuracy and effectiveness of object detection and data collection.
Innovation Solution
A system and method for sensor correlation using AI models and edge devices, where edge devices receive and analyze data to determine if detected objects are the same based on object parameters, and adjust sensor operations to improve data quality and accuracy by coordinating sensor operations across multiple edge devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor data from multiple dispersed edge devices is collected and analyzed, then object detection accuracy is improved, but system complexity and data correlation difficulty increase
Solution Approach 1:
The patent introduces a central system that acts as an intermediary to receive sensor data from multiple edge devices, perform correlation processing, and coordinate sensor operations. This intermediary system manages the complexity of correlating data from dispersed devices while maintaining improved detection accuracy through centralized analysis.
Solution Approach 2:
The patent combines sensor data from multiple edge devices and correlates information about detected objects across different devices. By merging data streams and combining detection results, the system achieves more accurate object identification while managing complexity through systematic integration.
2Reliability
If sensor operations are coordinated across multiple edge devices, then data quality is improved, but communication and synchronization requirements increase
Solution Approach 1:
The patent implements feedback mechanisms where the central system receives sensor data from edge devices, analyzes correlation results, and sends control signals back to coordinate future sensor operations. This feedback loop enables improved data quality through iterative optimization while managing synchronization complexity through centralized control.
Solution Approach 2:
The system performs preliminary coordination of sensor operations before data collection, pre-establishing synchronization protocols and communication channels. This preliminary action reduces the complexity of real-time synchronization by preparing the system in advance for coordinated multi-device operation.
Data Source
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AI summary
Systems and methods for performing sensor correlation by a plurality of edge devices are disclosed. For example, a method includes: receiving a first set of edge data from a first edge device of the plurality of edge devices; receiving a second set of edge data from a second edge device of the plurality of edge devices, the second edge device being different from the first edge device; analyzing the first set of edge data using one or more computing models to determine a first object detected in the first set of edge data; analyzing the second set of edge data using the one or more computing models to determine a second object detected in the second set of edge data; and determining whether the first object and the second object are a same object based upon one or more object parameters.