Machine Usage Visualization for Cross-Site Fleet Comparison
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Solution Overview
Problem
Managing a fleet of machines, such as excavators and bulldozers, is challenging due to the dispersion of relevant data across different systems, requiring manual data export and integration for comprehensive understanding, and there is a need for easy comparison of machine usage data across various domains.
Innovation Solution
A method and system for machine usage visualization that groups machines by job site and type, presenting graphical indicators of operating and idle time, machine faults, and status, using telematics data and geofencing to integrate data from multiple domains into a unified display.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data is gathered from multiple different systems and domains, then comprehensive machine information is obtained, but data integration complexity increases
Solution Approach 1:
The patent combines data from multiple different systems and domains (telematics data, job site data, maintenance data, operator data) into a single unified dashboard interface. This merging allows fleet managers to view all relevant machine information in one place, eliminating the need to manually access and integrate data from separate systems, thus reducing data integration complexity while maintaining comprehensive information.
2Loss of information
If manual data export and stitching is performed, then data comparison is possible, but time consumption increases
Solution Approach 1:
The system performs preliminary actions by automatically collecting, organizing, and pre-integrating data from multiple sources in the background before the user needs it. The dashboard is pre-populated with comprehensive machine data, comparisons are automatically calculated, and visualizations are pre-rendered, eliminating the need for manual data export and stitching operations at the time of analysis.
3Measurement precision
If detailed machine data is displayed, then performance analysis is improved, but information overload occurs
Solution Approach 1:
The dashboard applies local quality by providing different levels of data detail and visualization types for different machine parameters and user needs. Critical performance metrics are highlighted with visual indicators (color-coded status, trend arrows), while detailed raw data is available on demand. The interface adapts the presentation quality of information based on its importance and the context, making comprehensive data easy to interpret.
Data Source
AI summary
A method for machine usage visualization, the method including: receiving job site location information for a plurality of machines; receiving telematics data for multiple time periods from one or more of the plurality of machines, the data including: machine type, fault codes, and usage information; receiving a machine status for one or more of the plurality of machines; grouping the machines into machine groups by the received machine type; and grouping the machines into job site groups by the received job site location information.