Machine Status Calendar GUI with Inferred Missing Time Intervals
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Solution Overview
Problem
Existing systems for tracking mobile machine operations at worksites, such as farming, construction, or forestry, often rely on manual recording and lack consideration for complex interactions between terrain, ground conditions, soil type, and weather, leading to incomplete datasets and inefficiencies.
Innovation Solution
Implementing a system that uses machine learning or deep learning models to track mobile machine operational statuses at intervals of time, generating graphical user interfaces (GUIs) that provide a chronological view of operations, including environmental factors, and leveraging satellite data and machine telemetry to enhance data completeness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If manual recording methods are used to track mobile machine operations, then device complexity is reduced, but measurement precision and data completeness deteriorate due to human error and inconsistent recording
Solution Approach 1:
The mobile machine automatically tracks and records its own operational statuses through integrated sensors and telematics systems, eliminating the need for manual recording. The system self-monitors parameters such as location, speed, operational mode, and environmental conditions, thereby improving measurement precision while maintaining acceptable system complexity through automated self-service tracking.
2Measurement precision
If automated tracking systems are implemented, then measurement precision and data completeness improve, but device complexity and cost increase
Solution Approach 1:
The tracking system is designed as a universal multi-functional platform that can monitor various operational parameters (location, speed, operational mode, environmental conditions) using a single integrated architecture. This universal design improves measurement precision across all tracked parameters while managing device complexity by consolidating multiple monitoring functions into one cohesive system rather than requiring separate specialized systems for each parameter.
3Productivity
If operators focus on real-time decision making in the field, then productivity improves, but measurement precision deteriorates due to missed or inconsistent record-keeping
Solution Approach 1:
The system automatically performs record-keeping functions by self-monitoring operational parameters through integrated sensors and telematics, freeing operators to focus entirely on real-time decision making and productivity tasks. The automated system captures operational data without requiring operator intervention, thereby maintaining high productivity while improving measurement precision through consistent automated recording.
4Measurement precision
If complex interactions between terrain, ground conditions, soil type, and weather are considered, then measurement precision improves, but device complexity and computational requirements increase
Solution Approach 1:
The system introduces environmental sensors and data sources as intermediaries that automatically capture terrain, ground conditions, soil type, and weather parameters. These intermediary sensors gather environmental data without requiring complex manual analysis, and the system integrates this data with operational status tracking to improve measurement precision while managing complexity through standardized data collection and processing protocols.
Data Source
AI summary
Technologies for generating GUIs for tracked mobile machine operational statuses. In some embodiments, a method includes receiving, by a computing system, machine operational status information of a mobile machine that has moved through an area of land during a time period. The received machine status information including respective operational statuses of the machine at each interval of intervals of time within the time period. The method also including generating, by the computing system, a GUI according to the status information. The GUI including a calendar view of the statuses. In some cases, the operational status information includes respective operational statuses of the machine at only some intervals within the time period. And the statuses of the machine that are missing can be generated by a model that is trained by the available statuses received by the system.