Deep Learning Machine Settings for Fuel-Efficient 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 that increase fuel consumption and 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 efficiency, reduced soil compaction, and improved productivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If manual planning and simple computations are used to determine machine settings, then the system complexity is low, but the settings quality is subpar leading to increased fuel consumption
Solution Approach 1:
The patent replaces manual planning and simple heuristic algorithms with a machine learning-based computing system that processes complex interactions between terrain, ground conditions, soil type, weather, and machinery capabilities. This substitution enables optimization of fuel consumption by considering factors that simple computations cannot capture, while the system complexity increase is justified by the significant energy savings achieved.
2Productivity
If preselected settings are used without considering complex interactions, then the operational process is simple, but productivity is reduced
Solution Approach 1:
The patent implements a machine learning model that analyzes complex interactions between multiple factors (terrain, ground conditions, soil type, weather, machinery capabilities) to generate optimized machine settings. This replaces simple preselection methods with an intelligent system that considers all relevant factors simultaneously, thereby improving productivity while managing the complexity through automated computational processing.
Solution Approach 2:
The system dynamically adjusts machine settings parameters based on real-time conditions and learned patterns from training data. By changing parameters such as speed, depth, and other operational settings according to complex environmental interactions, the system optimizes productivity without requiring manual intervention for each condition change.
3Ease of operation
If manual determination of settings is performed, then the system is easy to operate, but the settings do not optimize efficiency and cost
Solution Approach 1:
The patent implements an automated machine learning system that independently determines optimized machine settings without requiring manual operator intervention. The system self-services by processing environmental data, applying trained models, and generating settings automatically, thereby maintaining ease of operation while significantly reducing operational costs through optimized efficiency.
Solution Approach 2:
The manual process of determining settings is replaced with an automated computing system that uses machine learning to optimize efficiency and cost. This substitution maintains ease of operation by eliminating complex manual decision-making while achieving superior cost optimization through consideration of complex factor interactions.
4Manufacturing precision
If simple computations are used for settings determination, then the computational resources required are low, but the settings quality is suboptimal
Solution Approach 1:
The patent replaces simple computational methods with a machine learning-based computing system that processes complex interactions between multiple factors. This substitution achieves superior settings precision by capturing non-linear relationships and complex interactions that simple computations cannot model, while managing computational complexity through efficient model architecture and training approaches.
Data Source
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, initial settings information, the initial settings information including settings used by or to be used by one or more mobile machines performing one or more tasks. 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, a deep learning model using the settings information. Also, in such examples, the method includes using, by the computing system, the trained model to generate new settings information for a given task.