Mobile Machine Control With Real-Time AI Settings Adjustment
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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 control and adjust settings of mobile machines in real time, taking into account various environmental and performance factors to optimize operations such as fuel consumption and productivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual planning and simple computations are used to determine mobile machine settings, then the system complexity is low and ease of operation is maintained, but the manufacturing precision of settings and productivity are reduced due to suboptimal settings
Solution Approach 1:
The patent replaces manual planning and simple computational systems with a deep learning-based artificial intelligence system. The deep learning model processes complex interactions between terrain, ground conditions, soil type, weather conditions, and machinery capabilities to generate optimized machine settings, substituting human operator expertise with an automated intelligent system that achieves superior settings precision while maintaining ease of operation.
Solution Approach 2:
The system changes the approach from static, preselected settings to dynamic, real-time setting adjustments based on multiple environmental and operational parameters. The deep learning model continuously analyzes variations in terrain, soil type, weather conditions, and machine performance to optimize settings parameters such as speed, fuel injection rate, and implement depth, thereby achieving both high precision and ease of operation.
2Ease of operation
If preselected settings are used without considering complex interactions between factors, then the ease of operation is maintained, but the loss of energy increases due to subpar settings and increased fuel consumption
Solution Approach 1:
The patent replaces simple computational systems with a deep learning-based AI system that automatically optimizes machine settings to minimize energy consumption. The model analyzes complex interactions between operational parameters and environmental conditions to determine optimal fuel injection rates, engine power settings, and implement configurations, thereby reducing fuel consumption while maintaining ease of operation through automated control.
Solution Approach 2:
The system implements real-time feedback mechanisms where the deep learning model continuously monitors machine performance data, environmental conditions, and fuel consumption patterns. Based on this feedback, the system dynamically adjusts settings to optimize energy efficiency, learning from actual operational outcomes to improve future settings and minimize fuel consumption while maintaining ease of operation.
3Device complexity
If manual determination of settings is used, then the device complexity is low, but the productivity is reduced due to suboptimal settings and increased operational costs
Solution Approach 1:
The patent replaces manual setting determination with an automated deep learning system that processes multiple data sources including terrain information, soil type, weather conditions, and machine performance data. This substitution increases system complexity but dramatically improves productivity by generating optimized settings in real-time, enabling faster operation speeds, reduced idle time, and more efficient resource utilization compared to manual methods.
Solution Approach 2:
The system performs preliminary analysis of environmental conditions, terrain characteristics, and operational requirements before determining machine settings. The deep learning model pre-processes and integrates multiple data sources to generate optimized settings in advance, allowing the machine to operate at peak efficiency from the start of each task rather than requiring trial-and-error adjustments during operation, thereby improving productivity.
4Ease of operation
If simple computations are used to suggest operational settings, then the ease of operation is maintained, but the reliability of settings is reduced due to inability to consider complex interactions between factors
Solution Approach 1:
The patent replaces simple computational algorithms with a deep learning-based AI system capable of modeling complex non-linear interactions between multiple factors including terrain variability, ground conditions, soil type, weather conditions, and machinery capabilities. The deep learning model processes these complex interactions to generate reliable optimized settings while maintaining ease of operation through automated decision-making, achieving both high reliability and user-friendliness.
Data Source
AI summary
Embodiments include technologies that use machine learning to control mobile machines as well as to adjust settings of mobile machines in real time (e.g., agricultural machine settings, construction machine settings, forestry machine settings, or landscaping machine settings). Some embodiments include a method that includes using a mobile machine to perform work in a field using first machine settings and recording performance information. The performance information indicating a performance of the mobile machine while performing the work in the field. Also, the method includes using a computing system to input the performance information into a trained deep learning model and to receive new machine settings information from the trained model, and using the computing system to control the machine according to the new settings information.