Views: 0 Author: Nursen Publish Time: 2025-12-23 Origin: https://www.luphitouch.com
The colorful panel serves as the core of intelligent interaction for the washing machine, integrating technologies such as sensors, AI algorithms, and IoT can assist users in completing laundry tasks from three stages: task planning, process optimization, and result feedback. Here are specific implementations and case analyses:
Technical Implementation:
Camera + Image Recognition: A built-in micro camera on the color panel scans clothing labels or stained areas, identifying materials (such as cotton, wool, silk) and stain types (e.g. oil stains, bloodstains, wine stains) through AI algorithms.
RFID Tag Reading: High-end models can be paired with smart washing tags. Users sew the tags into clothes in advance, and the panel reads the washing requirements (such as water temperature, spin speed limit) via NFC or RFID technology.
User Value:
Automatically matches the best washing program (such as "Wool mode requires low temperature and gentle") to avoid damage to clothes due to incorrect operation. Prescribes pre-treatment steps for stubborn stains (such as "Oil stains need to be rubbed with detergent and soaked for 5 minutes first").
Technical Implementation:
Weight Sensor Linkage: The panel and the washing machine's inner drum weight sensor communicate data with each other. Based on the weight and material of the clothes, it calculates the required amount of detergent and fabric softener and displays recommended values on the panel.
Liquid Level Monitoring and Automatic Dosing: Some models support liquid level monitoring in the detergent box. The panel reminds users to replenish consumables or links with the automatic dosing system to add as needed.
User Value: Prevents excessive or insufficient detergent residue, leading to poor cleaning results, and saves on consumable costs. Reduces the hassle of manual measurement for users, especially suitable for elderly users or novices.
Technical Implementation:
Voice/App Reservation: Users can reserve the washing time through voice commands on the panel or via a mobile app. The panel recommends the optimal start time based on the current peak and off-peak electricity periods and the user's schedule (e.g., "pick up after work").
Emergency Mode: When clothes that the user urgently needs to wear are detected (such as marked "urgent" by voice), the panel prioritizes washing and shortens the cycle.
User Value: Balancing clean efficiency with energy consumption costs, such as automatically starting up during off-peak hours at night when electricity prices are low. Coping with sudden demands and enhancing flexibility in time management.
Technical Implementation:
Dynamic Progress Bar + Animation Demonstration: The panel displays the washing stages (such as soaking, rinsing, and spinning) in real-time through a progress bar, 3D animation, or AR projection, and indicates the remaining time.
Inner Tub Camera Live Stream: High-end models allow users to view the status of clothes in the inner tub (e.g., whether they are tangled, foam level) via the panel, and users can remotely adjust the program.
User Value: Reduces the hassle of users frequently checking the lid, avoiding interruptions in the washing process. Prompt intervention when abnormalities (such as clothes getting stuck in the door gap) are detected, reducing the risk of damage.
Technical Implementation: Temperature and Humidity Sensor Linkage: The integrated environmental sensor on the panel detects the indoor temperature and humidity, automatically adjusting the dehydration speed (such as increasing the speed in humid weather to shorten drying time).
Water Hardness Detection: Through the water inlet water quality sensor, the panel recommends whether to add softener or adjust the amount of detergent used.
User Value: Optimize washing results, such as reducing the residue of detergent in hard water areas to prevent clothes from becoming stiff. Adapt to different geographical environments (such as humid south and dry north) to enhance user experience consistency.
Technical Implementation: Vibration/Noise Analysis: The panel monitors the washing machine's operational status through microphones and accelerometers, identifying abnormal vibrations or noises to diagnose fault types such as bearing wear or motor overheating. One-Touch Self-Repair
Guidance: When minor faults (such as a clogged drain pipe) are detected, the panel plays repair animations or provides voice guidance to help users resolve issues on their own, avoiding the cost of door-to-door maintenance.
Customer Value: Extend the life of washing machines, reduce interruptions in laundry due to sudden breakdowns. Lower after-sales maintenance costs and enhance customer trust in the brand.
Technical Implementation:
Image Comparison Analysis: Before and after washing, photos of clothes are taken through the inner drum camera, and AI algorithms on the panel compare the rate of stain removal and the degree of color fading, generating a cleanliness report.
Fiber Detection: High-end models can detect fiber wear of clothes and recommend subsequent care methods (such as "It is recommended to reduce high-temperature washing for this shirt").
User Value: Helps users understand the washing effect, adjust future washing strategies. Extend the service life of clothing and reduce frequent replacements due to improper washing.
Technical Implementation: Electricity/Water Consumption Statistics: The panel displays the real-time electricity consumption and water usage of the current wash cycle, comparing it with historical data or the average value of similar products. Carbon Offset Suggestions: Based on energy consumption, calculate carbon emissions and recommend carbon offset solutions (such as donating to afforestation projects) or eco-friendly modes (like "using cold water for the next wash can reduce XX grams of carbon emissions").
Customer Value: Meeting the needs of users with strong environmental awareness and enhancing the green image of the brand. Encouraging users to develop energy-saving habits and reduce long-term use costs.
Technical Implementation: Laundry Schedule Management: Based on the user's historical washing frequency and the quantity of clothes, the panel recommends the next wash time (e.g., "Washing is expected to be needed in 3 days").
Supply Refill Reminder: When detecting insufficient detergent or softener levels, the panel directly jumps to the brand's online shopping link to purchase, or recommends nearby offline refill points.
Customer Value:
Prevents interruptions in laundry due to the depletion of consumables, enhancing convenience in life. Creates new sales channels for brands to achieve "service as sales.
1、Haier Smart Wash System:
Through the color film panel + intelligent sensor, automatically identify the fabric and stains of clothes, match 18 custom programs, and link with the dryer and clothes rack to achieve full process automation. Users can view the clothing care report through the panel, including cleanliness, wear rate and other data.
2、AI DD Motor on LG ThinQ Washing Machine:
The panel display AI algorithm dynamically adjusts the washing actions (such as gentle tapping or vigorous rubbing) based on the weight and fabric of the clothes, reducing 18% damage to the clothes. It supports voice control, allowing users to inquire about the washing progress or adjust parameters at any time.
3、Xiaoji Color Film Washing Machine AR Laundry Guide: After scanning the clothing label, the panel overlays a 3D animation via AR technology to demonstrate the best washing method and records user preferences to form a personalized profile.
The intelligent auxiliary functions of the color film panel need to revolve around "reducing the burden of user decision-making, optimizing the washing process, and enhancing the controllability of the results". The core directions include:
1、Sensing Layer: Integrate more sensors (such as cameras, RFID, water quality detection) to achieve all-dimensional recognition of clothing and the environment.
2、Decision-making level: Transforming sensor data into executable suggestions through AI algorithms, replacing manual planning by users.
3、Execution layer: Link the washing machine hardware (such as motors, dispensing systems) and external devices (such as clotheslines, shopping malls) to form a closed-loop service.
In the future, as generative AI and IoT technology become more prevalent, smart washing machines may evolve into "laundry butlers" that proactively learn user habits and even predict needs (such as "suggesting advance washing based on weather forecasts"), completely reshaping the laundry experience.
