Automated Asset Identification Using Vehicle-Mounted Sensors
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Solution Overview
Problem
Manual data collection and analysis of electrical infrastructure assets, such as poles and equipment, is time-consuming and labor-intensive, with varying accuracy due to human involvement, necessitating an automated or semi-automated process for accurate asset identification and mapping.
Innovation Solution
A system utilizing sensors like cameras and GPS mounted on vehicles for real-time data capture and processing, combined with machine learning algorithms to identify and geotag assets, generate GIS maps, and integrate data for accurate location and condition assessment, capable of being used on various platforms including automotive vehicles and UAVs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data collection and analysis is used to identify and locate electrical infrastructure assets, then human expertise can be applied to interpret complex equipment, but the process becomes time-consuming and labor-intensive
Solution Approach 1:
The patent replaces manual mechanical inspection processes with automated optical sensing systems. Cameras and sensors mounted on vehicles automatically capture images and data of electrical infrastructure assets, eliminating the need for manual data collection while maintaining identification accuracy through image processing and pattern recognition algorithms.
Solution Approach 2:
The system enables self-service through automated asset identification and mapping capabilities. The vehicle-mounted sensors autonomously detect, classify, and geolocate electrical assets without requiring human intervention during the data collection phase, with results automatically processed and integrated into mapping systems.
2Reliability
If manual data collection by trained engineers is used, then accurate identification of electrical equipment can be achieved, but enormous investment in human resources is required
Solution Approach 1:
The patent substitutes human engineers with automated sensor systems and computer vision algorithms. The vehicle-mounted cameras and processing systems automatically identify and classify electrical assets, replacing the need for trained engineers to manually review photographs and extract asset information.
Solution Approach 2:
The system creates visual copies of physical assets through high-resolution imaging. Cameras capture detailed images of electrical equipment, which are then processed digitally to identify asset characteristics, replacing the need for human visual inspection while maintaining identification reliability.
3Productivity
If automated sensor systems are deployed on vehicles for real-time data capture, then productivity and speed of asset mapping are improved, but the system complexity and initial investment increase
Solution Approach 1:
The vehicle-mounted system is designed with multi-functionality to handle various asset types and data collection requirements. The sensor platform can identify different electrical infrastructure assets (poles, transformers, switches) and integrate multiple sensing modalities, reducing the need for separate specialized systems for each asset type.
Solution Approach 2:
The patent combines multiple functions into a single integrated vehicle-mounted system. Cameras, GPS, and processing units are merged into one platform that simultaneously performs data capture, asset identification, and geolocating, eliminating the need for separate manual processes and reducing overall system complexity through integration.
Data Source
AI summary
A system for asset identification and mapping is configured to information related to a plurality of objects with at least one sensor. The captured information may be processed to identify one or more assets among the objects, and a map generated including a geographic area proximate to at least one of the one or more assets. The at least one of the one or more assets may then be identified on the map.