Crowd-Sensed Data Processing for Infrastructure Aberration Detection
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Solution Overview
Problem
Existing infrastructure development and maintenance systems face limitations in processing crowd-sensed data due to constraints such as lighting conditions for image processing and robustness of training data in machine learning techniques, leading to inefficiencies in detecting aberrations in infrastructure.
Innovation Solution
A method and system for processing crowd-sensed data that involves receiving metadata from mobile devices, prioritizing events based on type, impact, and urgency, and transmitting notifications to responsible organizations, utilizing processors to validate and analyze data quality, completeness, and consistency, and employing machine learning techniques for event categorization and prioritization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If image processing techniques are used to detect infrastructure aberrations, then detection capability is provided, but the detection accuracy deteriorates under poor lighting conditions
Solution Approach 1:
The patent introduces crowd-sensed metadata as an intermediary data source that supplements traditional image processing. Users capture photos and provide descriptive metadata about infrastructure conditions, which serves as a mediator between the imaging system and the analysis system, enabling more reliable detection under varying lighting conditions
Solution Approach 2:
The system combines multiple data types (image data and metadata) into a composite dataset for analysis. This composite approach merges visual information with user-provided contextual information, creating a more robust detection system that overcomes the limitations of relying solely on image processing
2Extent of automation
If machine learning techniques are used for event detection, then automation is improved, but reliability deteriorates due to constraints in training data robustness
Solution Approach 1:
The system performs preliminary validation and quality assessment of crowd-sensed metadata before feeding data into the machine learning model. This preliminary action ensures that only high-quality, reliable data is used for training and detection, improving the overall reliability of the automated system
Solution Approach 2:
The patent implements a feedback mechanism where detected events are validated and prioritized based on multiple criteria including data quality, completeness, and consistency. This feedback loop continuously improves the system's reliability by learning from actual performance and adjusting detection thresholds and priorities
3Reliability
If comprehensive data processing is performed to ensure data quality and completeness, then detection reliability is improved, but processing time increases
Solution Approach 1:
The system applies different levels of validation and processing to different aspects of the data based on their importance. Critical fields such as event type and location receive rigorous validation, while less critical metadata undergoes lighter processing. This local quality approach ensures reliability for key detection parameters while minimizing overall processing time
Data Source
AI summary
The disclosed embodiments illustrate methods and systems for processing crowd-sensed data. The method includes receiving the crowd-sensed data from a mobile device associated with a user. The crowd-sensed data corresponds to metadata of an event pertaining to an aberration in at least one of a public service, a public infrastructure, a private service, or a private infrastructure. Thereafter, the event may be prioritized based at least on a type of the event, a measure of impact of the event, or a measure of urgency to resolve the event. Further, a notification of the event may be transmitted to an organization responsible to at least resolve the event, based on the prioritizing, wherein the notification comprises at least the metadata.