Dynamic Event Verification With Sensor Accuracy Selection
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Solution Overview
Problem
Existing methods for verifying events using sensor data often result in inaccuracies, leading to false determinations and inefficient resource allocation, as they lack effective mechanisms for cross-validation and sensor selection based on data accuracy and geographic compliance.
Innovation Solution
A computing platform that receives and compares source data from multiple sensors using machine learning algorithms, determines event occurrence by exceeding predetermined thresholds, and updates algorithms based on validation, while selecting sensors for data collection based on accuracy and compliance with geographic policies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If sensor data is used to determine event occurrence, then event detection capability is improved, but measurement accuracy deteriorates due to false determinations
Solution Approach 1:
The system implements feedback by comparing sensor data against historical event data and using the comparison results to verify event determinations. The computing platform receives sensor data, compares it with historical patterns, and uses this feedback loop to improve determination accuracy while maintaining detection capability.
Solution Approach 2:
The patent introduces an intermediary verification mechanism where a computing platform acts as a mediator between raw sensor data and event determinations. This intermediary compares sensor data with historical event data and applies verification logic to reduce false determinations before final event classification.
2Measurement precision
If multiple sensors are used for event verification, then event determination accuracy is improved, but device complexity increases
Solution Approach 1:
The computing platform performs multiple functions including receiving sensor data, comparing with historical data, verifying event determinations, and updating historical event data. This multi-functional approach consolidates what would otherwise require separate systems into a single versatile platform, reducing overall system complexity.
Solution Approach 2:
The patent merges sensor data reception, historical data comparison, event verification, and data updating into a single integrated computing platform. This consolidation combines multiple functions that could operate separately, simplifying the system architecture while maintaining verification capabilities.
3Reliability
If event verification processes are implemented, then false alarms are reduced, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-collecting and storing historical event data for comparison. When new sensor data arrives, the verification process compares against pre-prepared historical patterns, reducing the time needed for verification while maintaining reliability.
Solution Approach 2:
The patent changes parameters by adjusting comparison thresholds and verification criteria based on historical event data. This dynamic parameter adjustment optimizes the balance between verification thoroughness and processing speed, reducing false alarms without excessive time consumption.
Data Source
AI summary
Aspects of the disclosure relate to computing platforms that utilize improved techniques for dynamic event verification. A computing platform may receive first source data comprising driving data associated with a vehicle over a time period. Based on the first source data, the computing device may determine that the vehicle experienced an event, resulting in an event output. In response to determining the event output, the computing device may generate a request for second source data associated with the vehicle over the time period. The computing device may receive, from a sensor device, the second source data. Based on a comparison of the first source data to the second source data, the computing platform may determine an event comparison output. The computing platform may determine that the event comparison output exceeds a predetermined comparison threshold, and may send an indication of an event in response.