Work Machine Parameter Selection for Changing Slopes and Routes
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Solution Overview
Problem
Conventional devices struggle to efficiently manage energy consumption and travel parameters for work machines at sites where routes and gradients change frequently, such as mining sites, due to difficulties in updating map and altitude databases and analyzing dynamic travel patterns.
Innovation Solution
A parameter selection device and system that calculates average travel distances, fuel consumption, and production amounts based on travel history data to recommend optimal engine parameters, using sensors and communication systems to adapt to changing routes and slopes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional devices use static map databases and altitude databases to calculate fuel consumption, then the calculation method is simple, but the data cannot be constantly updated to reflect changing work site conditions
Solution Approach 1:
The system collects actual travel data including position, speed, slope, and fuel consumption from work machines, then feeds this information back to update the database. This feedback mechanism enables the system to adapt to changing work site conditions automatically without requiring manual database updates, resolving the contradiction between adaptability and system complexity.
Solution Approach 2:
The system performs self-updating by automatically collecting travel data from sensors on work machines and using this data to recalculate fuel consumption predictions. The database updates itself based on accumulated operational data, eliminating the need for external manual updates while maintaining high adaptability to changing conditions.
2Measurement precision
If the system collects and processes detailed travel history data to improve prediction accuracy, then the precision of fuel consumption prediction improves, but the data processing complexity increases
Solution Approach 1:
The system extracts only the essential parameters needed for fuel consumption prediction from the collected travel data, such as position, speed, slope, and fuel consumption. By focusing on extracting only the relevant data elements rather than processing all possible travel information, the system achieves high prediction accuracy while keeping data processing complexity manageable.
3Productivity
If the system analyzes travel patterns to provide optimized parameter recommendations, then operational efficiency improves, but the computational requirements and processing time increase
Solution Approach 1:
The system pre-calculates and stores optimal engine parameters and fuel consumption predictions based on historical travel data and various slope conditions. When a work machine needs recommendations, the system can quickly retrieve pre-computed optimization results rather than performing complex real-time calculations, thus improving operational efficiency while minimizing processing time delays.
Data Source
AI summary
The present disclosure provides a parameter selection device for a work machine that enables the work machine to efficiently travel at a work site where a travel route or a slope changes from moment to moment. The parameter selection device includes a parameter selection section. The parameter selection section calculates an average loaded travel distance per cycle, a travel ratio for each slope in loaded travel, and a virtual travel distance for each slope per cycle of the work machine (processing P1-P3). Further, the parameter selection section calculates a predicted fuel consumption amount for each slope per cycle, a travel time for each slope per cycle, a predicted fuel consumption amount per unit load weight, and a predicted production amount per cycle (processing P4-P8). Then, the parameter selection section selects a recommended parameter set, based on the predicted fuel consumption amount and the predicted production amount per cycle for each parameter set (processing P9).