Mobile Work Machine Control Using Qualified Predictive Data
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Solution Overview
Problem
Mobile work machines face challenges in selecting the most accurate predictive data for controlling operations due to varying accuracy levels and conditions, such as high-frequency and low-frequency conditions, which can affect operational efficiency and precision.
Innovation Solution
A system and method to determine the accuracy of multiple predictive data sources, identify a set of qualified data, and select one based on preset rules or selection conditions to control the mobile work machine effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple predictive data sources are used to improve accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system segments the data selection process into distinct functional modules: an accuracy determination module that evaluates predictive data accuracy using in-situ data, a qualification module that filters data sources based on accuracy thresholds, and a selection module that chooses among qualified sources. This segmentation manages complexity by organizing the multi-data-source evaluation into structured, manageable components rather than a monolithic system.
Solution Approach 2:
The system performs preliminary accuracy determination and qualification of predictive data sources before actual control decisions are made. By pre-evaluating data accuracy using in-situ measurements and establishing qualified data source lists in advance, the system reduces real-time computational complexity while maintaining high measurement precision in control operations.
2Productivity
If predictive data is used to improve operational efficiency, then productivity is improved, but reliability deteriorates due to varying data accuracy
Solution Approach 1:
The system employs feedback mechanisms where in-situ measured values are continuously compared with predictive data values to determine accuracy. This feedback loop allows the system to dynamically identify which predictive data sources are most accurate under current conditions and prioritize those sources, ensuring reliable and consistent control accuracy while maintaining high operational efficiency.
Solution Approach 2:
The system dynamically adapts its data source selection based on real-time accuracy assessments. Rather than using a fixed data source hierarchy, the system adjusts which predictive data sources are considered qualified and selected based on their current accuracy performance, ensuring reliability varies optimally with changing worksite conditions while preserving productivity.
3Measurement precision
If accuracy determination using in-situ data is implemented, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system applies partial action by determining accuracy only for predictive data sources that meet predefined qualification criteria rather than evaluating all possible sources equally. By selectively applying accuracy determination to a subset of promising data sources based on initial screening, the system achieves sufficient measurement precision without the excessive time cost of comprehensive evaluation of every available predictive source.
4Adaptability or versatility
If conditional selection of predictive data is implemented, then adaptability is improved, but device complexity increases
Solution Approach 1:
The system applies local quality by tailoring the data selection criteria and accuracy thresholds to specific worksite conditions and operational contexts. Different qualification rules and accuracy requirements can be applied locally based on the type of work being performed, the characteristics of the predictive data sources available, and the specific demands of the operation, enabling high adaptability without requiring a completely complex universal selection mechanism.
Data Source
AI summary
A computer implemented method includes: obtaining a plurality of predictive data, each predictive data indicative of respective predictive values of a characteristic corresponding to geographic locations at a worksite; obtaining in-situ data indicative of in-situ values of the characteristic corresponding to the geographic locations at the worksite; determining a respective accuracy of each predictive data based on the respective predictive values of the characteristic and the in-situ values of the characteristic; identifying, based on the respective accuracy of each predictive data, a set of qualified predictive data comprising two or more of the predictive data; determining whether a selection condition is present at the worksite; selecting one qualified predictive data, of the set of qualified predictive data, as selected qualified predictive data based on the determination of whether the selection condition is present at the worksite; and controlling a mobile work machine based on the selected qualified predictive data.