Autonomous Soil Dredger for Dynamic Trench Pattern Optimization
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Solution Overview
Problem
Current trenching methods in landscape channeling face challenges such as inefficient water absorption, soil erosion, and uncontrolled water runoff due to manual estimation of soil absorption rates and lack of optimal trench design, leading to surges and deficits in irrigation.
Innovation Solution
A cognitive, dynamic trenching system utilizing IoT and smart agriculture, which derives landscape metrics to generate digital copies of trench and ridge metrics, collaborating with an autonomous robotic soil dredger to create optimal trench patterns for water retention and soil absorption based on soil type, environmental conditions, and vegetation water consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual estimation of soil absorption rates is used for trench design, then the design process is simple, but water absorption efficiency is poor and uncontrolled water runoff occurs
Solution Approach 1:
The patent replaces manual estimation with automated sensor-based measurement systems that detect soil absorption rates, moisture levels, and other landscape metrics. This substitution of mechanical/manual processes with electronic sensing and data processing systems resolves the contradiction by providing precise measurements without requiring complex manual design procedures.
Solution Approach 2:
The system enables the landscape itself to 'measure' its own absorption characteristics through embedded sensors that continuously monitor soil moisture and absorption rates. This self-measuring capability eliminates the need for external manual estimation while providing accurate, real-time data for optimal trench design.
2Reliability
If optimal trench patterns are created using cognitive computing and digital twins, then water retention and soil absorption are optimized, but the system complexity increases
Solution Approach 1:
The patent creates digital twin copies of the physical landscape and trench systems, allowing virtual simulation and optimization of trench patterns before physical implementation. This copying approach enables reliable water retention optimization in the virtual model, which can then be replicated in the physical system without requiring the full complexity of real-time cognitive processing during actual trenching operations.
Solution Approach 2:
The system performs preliminary cognitive computing and optimization in advance to generate optimal trench designs, then executes the predetermined pattern using autonomous equipment. This preliminary action separates the complex optimization phase from the simple execution phase, achieving reliable water retention without maintaining continuous system complexity.
3Productivity
If autonomous robotic soil dredgers are used to create trenches, then labor efficiency is improved, but the initial investment and system complexity increase
Solution Approach 1:
The autonomous robotic soil dredger is equipped with sensors and navigation systems that allow it to autonomously locate optimal trench positions, dig trenches according to predetermined patterns, and monitor its own performance. This self-service capability eliminates the need for continuous human operation and supervision, achieving high productivity while reducing ongoing operational complexity.
Solution Approach 2:
The patent replaces manual labor with autonomous robotic systems that automatically perform trenching operations. This substitution of human mechanical labor with automated robotic machinery resolves the contradiction by achieving high productivity through automation while managing complexity through standardized, programmable operations.
4Adaptability or versatility
If dynamic adjustment of trench patterns based on real-time landscape metrics is implemented, then water management is optimized, but the control system complexity increases
Solution Approach 1:
The patent implements dynamic trench patterns that can be adjusted based on real-time sensor data about soil moisture, absorption rates, and landscape conditions. The system transitions from static predetermined patterns to dynamic adaptive patterns that respond to changing environmental conditions, resolving the contradiction between adaptability and complexity through feedback-driven adjustment.
Solution Approach 2:
The system incorporates continuous feedback loops where sensors monitor landscape metrics and soil conditions, and this information is used to dynamically adjust trenching operations in real-time. This feedback mechanism enables adaptive water management while managing complexity through closed-loop control that automatically responds to measured conditions without requiring complex predictive modeling.
Data Source
AI summary
A computer-implemented method for dynamic landscape channeling using an autonomous robotic soil dredger. The method derives one or more landscape metrics, wherein the one or more landscape metrics comprise soil metrics, environmental conditions, and vegetation water consumption metrics. The method further generates a digital twin of proposed trench and ridge metrics based on the derived one or more landscape metrics, wherein the proposed trench and ridge metrics comprise a depth of the trench, a distance between one or more trenches, a stream size, a slope of the trench, and a shape of the trench. The method further determines a trench pattern based on the generated digital twin of the proposed trench and ridge metrics and collaborates with the autonomous robotic soil dredger to generate the trench pattern.