Lesson 5 - Data Visualization
Students learn how sensor data is turned into live line charts on a visualization panel; then they use AI to turn it into a decision.
I can log into the SIoT website and see my data arrive
I can view the data as live line charts on the visualization panel
I can use AI to analyse the data and make a decision
1. Engage: Which Hydroponic Data Matters?
In the last lesson, we built a system that can pump water all by itself. But how do we know the plant is happy? How do we know the sunlight is bright enough, the temperature is right, and when the water will run out? We cannot tell from a single number. We have to watch how things change over time.
Key Question
In a hydroponic setup, which data is key and useful?
Guide students to brainstorm, then sort their ideas into the three most important kinds of data for our water-grown plant:
Light - Plants need light for photosynthesis, and its strength changes with the weather and the time of day.
Temperature - If it gets too hot or too cold, the plant stops growing well.
Water level - It drops a little every day, and we need to know when to refill.
A single reading is only a snapshot. To really understand what is happening, we need to see the data again and again over time. Today we will make our UNIHIKER K10 send this data over the network and let us watch it live.
2. Explore: How Does Data Get from the Sensor to the Display?
What is SIoT?
SIoT is an open-source, free MQTT server software designed for school settings, and it can create a local IoT server with a single click.
Download link: https://drive.google.com/drive/folders/16Lf0itQ-v2oZrUJEFHrOhjfgS68eN6bn?usp=sharing
The K10 sends its readings over the network. The path is: sensor (hardware) → program (software) → publish to an MQTT topic on the SIoT platform → the data appears on the SIoT website and can be drawn as charts on the visualization panel.
Please visit this website for more info: https://mindplus.dfrobot.com.cn/dashboard
3. Engineer: Send Data and See It on the SIoT Website
The teacher demonstrates the whole process while the students watch; they then try the same steps themselves.
Step 1: Open the SIoT website
Double-click start SIoT.bat to launch SIoT.
Record your PC’s IP address shown in the terminal.
Open the SIoT website (http://127.0.0.1:8080/) in a web browser and log in (username: siot, password: dfrobot).
Create a new topic: light.
Step 2: Make the network connection
Fill in the correct Wi-Fi name (account) and password to enable the K10 to connect to the network.
Teacher Note: the UNIHIKER K10 and the PC should connect to the same LAN.
Step 3: Set the MQTT parameters
In Mind+, set the MQTT initial settings, then connect to MQTT.
Fill in the Server address (copy it from the terminal).
Send a message to a topic (siot/light ) every second.
Upload the program to your UNIHIKER K10.
Watch the messages arrive under each topic by clicking “See details”. Cover and uncover the light sensor and see the number change.
4. Experience & Challenge: Turn the Data into Charts
Experience: Make the network connection and check the data on SIoT
Activity 1
• Guide students to launch SIoT and copy the IP address shown in the terminal.
• Ask students to modify the program to send messages for temperature and water level on the SIoT platform.
Sample code:
Challenge: Open the visualization panel
Activity 2
• Open the local dashboard file (dasgboard.mpui) in Mind+ to display the line charts.
Key question
Why is the line graph a good choice for time-series data compared with other chart types?
Teacher Note: If there is no data on the dashboard, please check the connection.
5. Elaborate: Let AI Read Our Data
Now we let the story that the data tells help us decide. Working in groups, read the values from the charts (light, temperature, and water level) and send them to a large-language model (for example, ChatGPT) to help analyse the data.
Key question
How long until the water needs to be topped up?
Guide students to download the water-level CSV file, then ask AI to read the data and suggest what to do.
Teacher Note: Before asking AI about the water-level trend, make sure you have collected data for a long enough period—a few minutes of data will not show a clear downward trend.
Lesson 5.zip 
