Predictive Data Selection for Mobile Work Machine Control
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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, making it difficult to optimize performance in high-frequency and low-frequency scenarios.
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
1Reliability
If multiple predictive data sources are used to control mobile work machines, then the adaptability and reliability of operations improve, but the complexity of data selection and processing increases
Solution Approach 1:
The system implements feedback by continuously monitoring the accuracy of predictive data sources and using this information to dynamically adjust data selection. The controller compares predictive data with actual operational outcomes and uses this feedback to identify qualified data sources, thereby improving reliability while managing complexity through intelligent adaptation.
Solution Approach 2:
The system changes parameters by evaluating different accuracy thresholds and selection criteria based on operational conditions. By adjusting the stringency of qualification criteria and weighting different data sources dynamically, the system adapts to varying operational requirements without requiring complex manual configuration.
2Measurement precision
If predictive data accuracy is continuously evaluated and optimized, then the measurement precision improves, but the loss of time for data processing increases
Solution Approach 1:
The system performs preliminary action by pre-evaluating and qualifying predictive data sources before they are needed for critical operations. By establishing a pool of qualified data sources in advance and continuously updating their status, the system reduces real-time processing requirements while maintaining high measurement precision when decisions are needed.
Solution Approach 2:
The system applies partial action by selectively evaluating only the most critical accuracy parameters for each data source rather than进行全面 analysis. This targeted approach achieves sufficient measurement precision for operational decision-making while significantly reducing the time investment required for continuous evaluation.
3Adaptability or versatility
If the system selects predictive data based on multiple selection conditions, then the adaptability to different working conditions improves, but the device complexity increases
Solution Approach 1:
The system implements dynamics by making the data selection process adaptive and flexible rather than static. The controller dynamically adjusts which data sources are qualified and selected based on current operational conditions, allowing the system to adapt to varying working environments while managing complexity through rule-based automation rather than rigid hardcoding.
Solution Approach 2:
The system achieves universality by creating a multi-functional data selection framework that can handle various types of predictive data sources and operational conditions through a unified qualification process. This universal approach allows the same selection logic to serve multiple functions across different working conditions without requiring separate complex systems for each scenario.
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.