Deep Learning Machine Settings for Terrain-Adaptive Mobile Equipment
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Solution Overview
Problem
Existing systems for determining settings for mobile machines, such as agricultural or construction equipment, do not adequately consider complex interactions between factors like terrain, ground conditions, soil type, weather, and machinery capabilities, leading to suboptimal settings and increased operational costs.
Innovation Solution
The use of machine learning, specifically deep learning models, to generate, update, or enhance machine settings for mobile machines, taking into account various factors to optimize operations such as fuel consumption, operation time, and soil compaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If manual planning and simple computations are used to determine machine settings, then the system complexity is low, but the settings quality and operational efficiency deteriorate due to inability to consider complex interactions
Solution Approach 1:
The patent replaces manual planning and simple computational systems with a machine learning-based system that processes multiple factors (terrain, ground conditions, soil type, weather, machinery capabilities) to generate optimized machine settings. This substitution enables the system to consider complex interactions between factors that manual methods cannot capture, thereby improving operational efficiency while managing complexity through automated algorithms.
2Loss of energy
If preselected settings are used without considering complex interactions, then the operational cost increases due to subpar settings, but the decision-making process remains simple
Solution Approach 1:
The patent employs a machine learning model that substitutes simple settings selection with an intelligent system capable of analyzing multiple interacting factors (terrain, ground conditions, soil type, weather, machinery capabilities). This enables the system to generate settings that optimize fuel consumption and reduce energy loss while accounting for complex interactions that would be intractable for traditional computational approaches.
Solution Approach 2:
The system dynamically adjusts machine settings parameters based on learned patterns from training data that incorporates various operational conditions. By changing parameters such as speed, depth, and other machine settings based on the interaction of multiple factors, the system optimizes fuel consumption and energy efficiency for specific operational contexts.
3Productivity
If manual determination of machine settings is performed, then the implementation cost is low, but the settings quality and operational productivity deteriorate
Solution Approach 1:
The patent replaces manual settings determination with an automated machine learning system that processes multiple factors to generate optimized settings. This substitution significantly improves operational productivity by providing data-driven recommendations that account for complex interactions between terrain, ground conditions, soil type, weather, and machinery capabilities, thereby reducing trial-and-error and optimizing operational outcomes.
Solution Approach 2:
The system enables self-service operation by automatically generating machine settings based on input data without requiring manual expert intervention. The machine learning model processes operational parameters and autonomously determines optimized settings, reducing dependency on manual planning while improving productivity through consistent, data-driven decision-making.
Data Source
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AI summary
Embodiments include technologies that use machine learning to enhance machine settings (e.g., agricultural machine settings, construction machine settings, forestry machine settings, or landscaping machine settings). Some embodiments include a method that includes using machine learning to generate or update machine settings. In some examples, the method includes receiving, by a computing system (102, 200), initial settings information (104), the initial settings information including settings used by or to be used by one or more mobile machines performing one or more tasks (step 302). The mobile machine(s) can include machines for farming, construction, forestry, or landscaping. In such examples, the method also includes training, by the computing system(102, 200), a deep learning model (106) using the settings information (104) (step 304). Also, in such examples, the method includes using, by the computing system (102, 200), the trained model (107b) to generate new settings information (108) for a given task (step 306).