Dishwasher with cloud connected cameras
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Solution Overview
Problem
Modern dishwashers face challenges in detecting and responding to overturned containers during the wash cycle, especially with light plastic items, which can lead to incomplete cleaning and water retention, particularly in systems with reduced water volumes.
Innovation Solution
A system utilizing multiple sensors within the dishwasher cavity, including cameras, RF sensors, and LIDAR, captures and analyzes imaging data to detect the position and arrangement of dishes, alerting users to adjust items and modifying wash cycles based on load size and arrangement, using deep learning algorithms for image analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If limited vision systems are used to estimate dishware position, then the system complexity is reduced, but the detection precision is insufficient to detect overturned containers
Solution Approach 1:
The vision system is divided into multiple cameras positioned at different locations within the dishwasher cavity, each capturing specific regions. This segmentation allows comprehensive coverage and enables detection of overturned containers through multi-angle imaging without requiring a single complex sensor system.
Solution Approach 2:
Image processing algorithms and deep learning models serve as intermediaries between the raw camera images and the control system. These intermediaries analyze the captured images to detect overturned containers and translate visual data into actionable control signals, bridging the gap between simple vision sensors and intelligent control.
2Productivity
If water is sprayed throughout all areas regardless of content, then the cleaning coverage is maximized, but the energy consumption increases
Solution Approach 1:
The spray system is controlled to deliver water locally to specific areas where dishware is detected, rather than uniformly across the entire cavity. The control system adjusts spray intensity and direction based on the detected load distribution, ensuring adequate cleaning coverage while minimizing energy consumption in empty or lightly loaded areas.
Solution Approach 2:
The spray pattern and water distribution are dynamically adjusted during the wash cycle based on real-time feedback from the vision system. The system can modify spray trajectories and intensities to adapt to the actual dishware arrangement, optimizing both cleaning effectiveness and energy efficiency throughout the cycle.
3Loss of energy
If the wash cycle is modified based on detected dishware load, then the energy efficiency is improved, but the device complexity increases
Solution Approach 1:
The vision system continuously monitors the dishware load and provides feedback to the control system, which then adjusts wash cycle parameters accordingly. This closed-loop feedback mechanism enables energy-efficient operation by adapting water and energy consumption to the actual load size and arrangement, while the modular architecture keeps the added complexity manageable.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system effectively detects overturned items, ensures complete cleaning by adjusting wash cycles, and optimizes detergent dosing, reducing water retention and improving wash efficiency.
Implementation Method 1
LIDAR sensors (light detection and ranging) to detect and determine dish ware location and arrangement
Data Source
AI summary
Systems and methods for monitoring dish ware position and modifying the wash and dry cycle of a dishwasher by capturing and analyzing image data utilizing a plurality of cloud-connected cameras.