Work Segment Tracking via Smart Machine Sensor Data
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Solution Overview
Problem
Conventional methods for tracking machine activity at worksites are hindered by the expense, difficulty in coordination, and susceptibility to failure of sensors on multiple work machines, particularly in fleets of both smart and non-smart machines.
Innovation Solution
A system and method that utilize a smart machine to collect and transmit detailed sensor data, allowing the characterization of its work segments, which are then used to associate and identify corresponding work segments of dumb machines based on location information, enabling real-time tracking and characterization of work cycles without the need for extensive sensor infrastructure on dumb machines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensors are installed on multiple work machines to track machine activity, then measurement precision of work segments is improved, but device complexity and cost increase
Solution Approach 1:
The patent creates a virtual copy of the worksite environment through geofences that replicate physical work segments. Instead of installing sensors on every machine, the system uses location information from dumb machines and compares it against the geofenced areas to identify work segments, effectively copying the spatial structure of the worksite into a virtual monitoring framework.
Solution Approach 2:
The patent introduces geofences as an intermediary layer between the dumb machines and the monitoring system. The geofences act as virtual boundaries that mediate the interaction between location data and work segment identification, eliminating the need for direct sensor installation on each machine while maintaining accurate tracking.
2Reliability
If sensors are installed on multiple work machines, then reliability of fleet-wide monitoring is improved, but ease of operation deteriorates due to coordination and calibration difficulties
Solution Approach 1:
The patent extracts the monitoring function from the machines themselves and places it in the central system through geofence-based tracking. By removing sensors from dumb machines and concentrating the monitoring capability in the central system that processes location data against pre-defined geofences, the system eliminates coordination and calibration issues while maintaining reliable fleet-wide monitoring.
Solution Approach 2:
The geofence system serves as a universal monitoring framework that works with all machines in the fleet regardless of whether they are smart or dumb. The same geofence boundaries and location-based identification method apply uniformly across the entire fleet, eliminating the need for machine-specific sensor configurations and simplifying operations.
3Loss of information
If sensors are installed on all work machines, then information completeness about work cycles is improved, but loss of time for calibration and setup increases
Solution Approach 1:
The patent performs preliminary action by pre-defining geofences that represent work segments before any machine operations begin. The geofence boundaries, locations, and associations with specific work segments are established in advance, eliminating the need for real-time calibration or setup when machines are deployed. This preliminary configuration enables immediate tracking without time-consuming calibration procedures.
Data Source
AI summary
A system and method for tracking activity of a plurality of machines can comprise identifying at least one work segment of a smart machine based on sensor data from one or more sensors of the smart machine; and identifying at least one work segment of each of a plurality of non-smart machines based on the identified at least one work segment of the smart machine and location data transmitted from the non-smart machine. The identified at least one work segment of the smart machine can be identified in association a first location at a worksite. The at least one work segment of each of the non-smart machines can be identified when the non-smart machine is within a predetermined distance of the first location associated with the identified at least one work segment of the smart machine.