乱穿衣 — Smart Outfit Recommendation | Panor
乱穿衣 (Smart Outfit Recommendation) is a free weather-aware outfit recommender from Panoramic Intelligence. It solves the classic Chinese 二八月乱穿衣 problem — those transition months when temperature swings from morning to afternoon make dressing impossible to plan. The tool fuses real-time weather, wind chill, humidity, and your personal cold-tolerance profile to suggest a layered outfit you can actually wear all day.
Who it is for
Anyone who has stepped outside in spring or autumn dressed wrong — students walking to early classes, commuters with long subway transfers, parents dressing kids for school, and travellers landing in an unfamiliar climate.
How the recommendation works
- Real-time weather inputs — temperature, perceived temperature, humidity, wind speed, UV, and rain probability for your city.
- Personal cold-tolerance profile — three settings (cold-sensitive, average, heat-sensitive) calibrate the algorithm to your body.
- Activity context — sitting indoors all day, commuting, outdoor sport, or evening event change the recommendation.
- Layering logic — base + mid + outer layers chosen so you can shed or add as the day changes; rather than a single jacket suggestion.
- Visual outfit cards — flat-lay illustrations of each suggested combination, plus textual explanations of why each piece is chosen.
Why this exists
Generic weather apps say "18°C" and stop. They don't account for the fact that 18°C with 80% humidity and wind feels colder than 18°C dry, or that a morning at 8°C will become 22°C by lunch. 乱穿衣 treats outfit-planning as a small optimisation problem: minimise discomfort across the day's temperature curve given your wardrobe layering options.
FAQ
Is it free? Yes, free to use in the browser. No login required.
Does it know my wardrobe? The free version recommends generic layer types (e.g., "light wool sweater + windbreaker"). A future version will let you photograph your closet for personalised picks.
Cities supported? All major Chinese cities and most international cities through OpenWeather data.
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