Physical weather station: Like a 'weather scout' in the neighborhood, squatting downstairs measuring temperature.
Many neighborhoods are quietly installing palm-sized white devices, standing in flower beds or on balconies; this is CAILA's 'Marco Weather Station', which is simply a super mini 'weather scout'. It doesn't need to be plugged in; there's a solar panel on the top, and it can work just by soaking up the sun, similar to the solar calculator you use for grocery shopping.
This little guy hides several 'little eyes' inside:
• Temperature sensor: It 'feels' hot and cold like you touching a watermelon, with a measured temperature that differs from the actual by no more than 1 degree;
• Humidity sensor: Can detect moisture in the air, more accurate than you checking if clothes are drying.
• Rainfall sensor: During rain, it acts like a small funnel, able to calculate how much water drips every minute.
The most amazing thing is that these 'scouts' do not work alone! Weather stations in neighborhoods, parks, and along streets form a network (DePIN network), just like you and other grocery shoppers asking each other where the freshest vegetables are; they summarize the data together, ultimately calculating the most accurate weather conditions for our area. In the past, the TV would say 'cloudy across the city', but now it can be precise to 'it's 2 degrees lower downstairs in our neighborhood than on the next street', which is much more reliable than looking at the old calendar!
Semantic intelligent agent: A 'weather assistant' that speaks human language, helping you plan things.
Do you know the voice assistant on your phone (like calling out 'Xiao Ai, classmate')? CAILA's intelligent agent is smarter than that, like a 'weather assistant' that follows you, can understand conversation, provide suggestions, and the more you use it, the more it understands you.
For example, when buying vegetables:
• Before you leave in the morning, if you say 'What vegetables are suitable to buy today?', it will first 'listen' to the data from the Marco Weather Station: 'Today is 32℃, humidity 80%, there might be some rain in the afternoon'.
• Then it starts to think: 'It's hot and humid, leafy greens are likely to wilt, I should recommend durable potatoes and winter melons; it might rain in the afternoon, so I should remind you to bring an umbrella and check if there are discounts at the nearby supermarket'.
• Finally tells you: 'Aunt Zhang, it's warm today, let's buy winter melon to make soup to cool off! It might rain lightly at 4 PM, remember to bring an umbrella, and the potatoes at the supermarket downstairs are 20% off~'
This 'assistant' also 'learns': If you often buy tofu on rainy days (because it doesn't spoil easily), the next time it rains, it will proactively suggest, 'Do you want to bring some tofu along?', more considerate than your daughter.
The relationship between the physical weather station and the intelligent agent is like when you go grocery shopping, and an old neighbor helps you check the weather at the market entrance while you pick vegetables inside.
1. The weather station first 'runs errands' to collect data: squatting downstairs to measure temperature and humidity, like an old neighbor standing at the door shouting, 'It's sunny today, bring a hat!'
2. Intelligent agent 'calculates' suggestions: Transforms data into human language, telling you 'It's sunny, choose firm tomatoes when buying, otherwise they'll spoil easily', and can even calculate 'It might rain in the afternoon, buying fish now is the freshest choice'.