Dishwasher with cloud connected cameras
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Solution Overview
Problem
Modern dishwashers face challenges in detecting and adjusting for 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 reorient items and adjusting wash cycles based on load size and arrangement, using deep learning algorithms for image analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If limited vision systems are used to estimate dishware position, then the system can approximate dishware location, but it cannot detect overturned containers during the wash cycle
Solution Approach 1:
The system captures an initial image of the dishware load before the wash cycle begins. This preliminary action establishes a reference state that can be compared against subsequent images during the wash cycle to detect any changes, including overturned containers.
Solution Approach 2:
The system continuously captures images during the wash cycle and compares them with the initial reference image. This feedback mechanism allows the system to detect changes in dishware position and identify overturned containers by analyzing differences between the reference state and current state.
2Quantity of substance
If predefined wash functions spray water throughout all racks, then all areas receive water coverage, but energy is wasted on areas without dishware
Solution Approach 1:
The system identifies specific locations where dishware is present and directs water spraying only to those localized areas. Instead of uniform water distribution across all racks, the system adjusts spray patterns to match the actual dishware configuration, providing targeted cleaning where needed and conserving energy where not needed.
Solution Approach 2:
The wash system dynamically adjusts water spraying based on real-time detection of dishware positions. The spray patterns, intensity, and duration are modified according to the detected load configuration, allowing the system to adapt water distribution to actual cleaning needs rather than following a fixed predefined pattern.
3Reliability
If multiple sensors and deep learning algorithms are implemented to detect overturned items, then detection capability is improved, but device complexity increases
Solution Approach 1:
The system uses image processing algorithms as an intermediary to translate raw sensor data into meaningful information about dishware positions and overturned containers. Rather than requiring complex direct sensing of container orientation, the intermediary image analysis process simplifies the detection task by working with visual data that naturally captures the state of dishware.
Solution Approach 2:
The system creates a digital copy of the dishware load through imaging and uses this copy for analysis. By working with image data representations rather than directly manipulating physical sensors for each detection task, the system simplifies complexity while maintaining detection accuracy. The digital model allows flexible analysis of container orientation and position.
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, reduces water retention, and optimizes wash cycles by customizing detergent dosing and water distribution, ensuring better cleaning efficiency and system performance.
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.