"The best technological innovation often lets users not feel the technology's presence, only enjoy the better life it brings."
Making home refrigerators AI-smart is not function stacking but using technology to solve users' freshness anxiety of 'dare not stock up, cannot manage, cannot keep.' When most fridges still compete on "frost-free cooling," Meiling's Fresh-Freeze 505, with a 99.7% frozen-meat juice-retention rate and a 15-day leafy-vegetable freshness period, redefined "the certainty of freshness." Meiling's AI freshness technology did not fall into the "function-stacking" innovation trap, but precisely captured refrigerator users' three core to-do tasks, and through a closed loop of "task decomposition – technology adaptation – value validation," achieved a paradigm upgrade from "parameter innovation" to "task innovation."
01 The First Principles of Home-Appliance Innovation: User To-Be-Done Tasks
Clayton Christensen's JTBD (Jobs To Be Done) theory holds that users choose a product essentially to accomplish a to-be-done task in a specific situation. When buying a fridge, a user is "hiring" it to accomplish the life task of "keeping ingredients fresh and controllable, using space efficiently, and operating without worry." Meiling's practice of rebuilding the freshness logic with AI offers the home-appliance industry a "task-oriented" innovation template.
1. Cracking the "Game Between Time and Freshness"
Research shows ordinary fridges lose up to 50% of frozen food's umami components—behind this is users' core need to "still eat near-fresh food after stocking up." Traditional freezers' ±2°C temperature swings cause ice crystals to form repeatedly, piercing cell membranes and draining nutrients, trapping users in the dilemma of "either frequent shopping or accepting waste."
2. Breaking the "Gap Between Technology and Experience"
Freshness and cooling effect is one of consumers' three top concerns about fridge functions. But traditional fridges' complex operation deters users: most products offer 20+ modes, yet only 12% of users can correctly use the granular functions, and 83% only use "auto mode" long term—reflecting users' latent need to "get the best result without learning."
The essence of these contradictions is the disconnect between "hardware capability" and "user need": a fridge can cool, but cannot answer concrete questions like "how long should this steak be frozen so it isn't dry?" or "how do I store the spinach for tomorrow?"
02 Meiling's AI Fresh-Freeze: Using AI to Improve User Experience
Meiling's AI innovation is not a single-point breakthrough but a value closed loop of "data perception – model decision – hardware execution," with each link precisely meeting user pain points: "freshness is not luck but the certainty of technology." The value of home-appliance AI lies not in "conversing or connecting to the internet" but in "using the certainty of data + hardware to eliminate the uncertainty in users' lives"—this is the product-innovation logic that rides through tech cycles.
1. Data Perception: Let the Fridge "See" Ingredient Needs
The Fresh-Freeze 505 fridge carries 8 sets of temperature-humidity sensors (precision ±0.1°C) and gas sensors, building a multi-dimensional monitoring network that captures in real time key data like in-box temperature swings and ethylene concentration. A door pressure sensor identifies ingredient weight, and combined with usage-frequency data it judges user habits, providing a precise basis for later decisions. This "all-weather monitoring" capability breaks the traditional fridge's "freshness black box," making ingredient status visible.
2. Model Decision: AI Algorithms Replace "User Choice"
Relying on Changhong's Yunfan AI large model, Meiling developed "AI self-optimization technology." By learning 30 days of user behavior, it auto-generates personalized plans: detecting frequent nighttime access, it switches to silent mode from 22:00 to 6:00; detecting a weekend stocking peak, it switches to strong-freeze mode early. For different ingredients, the model auto-matches the optimal storage plan, achieving "thousand-people-thousand-faces" precise freshness.
3. Hardware Execution: Hardware Innovation Delivers the Experience
The top-mounted constant-temperature fresh-freeze system is Meiling's core breakthrough. By optimizing the full cycle of cooling, stopping, and defrosting, it lowers defrost-temperature rise by 55% and keeps ingredient temperature swing below 0.1°C, fully avoiding the ice-crystal zone. Its 599 mm ultra-slim body perfectly matches Chinese households' 600 mm standard cabinets; with bottom heat dissipation, it achieves a "zero-embedded" effect needing only 2 cm side gaps, resolving the space-aesthetics conflict. The −32°C deep-freeze technology lets meat pass through the ice-crystal zone quickly, and with a quality thawing device it finally achieves a 99.7% thaw-juice retention rate.
Good product-innovation design inevitably brings market growth returns: in H1 2025 Meiling's revenue reached RMB 18.072 billion (+20.80%), and the Fresh-Freeze series lifted its high-end market share by 15%!
03 Lessons from the Case: A Replicable Product-Innovation Path
Meiling's practice reveals three core laws of home-appliance AI-ization, offering the industry a replicable product-innovation path.
1. Task Definition Precedes Technology Choice.
Meiling did not blindly chase the parameter race but focused on concrete tasks like "frozen-meat blood water" and "embedded heat dissipation," using an 8-billion-parameter scenario model for precise breakthroughs. As Changhong Meiling emphasized at the launch: "freshness is not luck but the certainty of technology"—this "task-anchoring" thinking avoids directional waste in technology innovation.
2. Closed-Loop Capability Outweighs Single-Point Breakthrough.
Meiling's competitiveness lies in the synergy of "perception – decision – execution": from sensors identifying ingredients, to the model generating strategy, to the evaporator precisely controlling temperature, forming an inseparable value chain. Standalone AI voice or sensors cannot solve the task; only a closed-loop system turns technology into experience.
3. Experience Standards Replace Parameter Standards.
Meiling uses user-perceivable metrics like "thaw-juice retention rate" and "vitamin C retention rate" as the basis for technology iteration, rather than merely competing on cooling capacity or volume. This idea of "defining technical requirements by task results" closely matches the healthy-refrigerator evaluation direction promoted by the China Consumers Association, leading the industry from "parameter orientation" to "experience orientation."
Meiling's AI freshness innovation logic is essentially a model of "user tasks driving technology evolution." As the fridge evolves from a "cooling machine" into a "freshness steward," the lesson is: the ultimate goal of home-appliance AI-ization is not to make products smarter but to make users more carefree—after all, the best technological innovation often lets users not feel the technology's presence, only enjoy the better life it brings.
