In LoRaWAN deployments, fast data decoding and data model configuration are critical to project efficiency. ThinkLink fully supports TTN and ChirpStack decoder scripts, allowing direct reuse of manufacturer-provided code.
This article demonstrates the configuration process using the EM300-SLD water leak sensor from Milesight.
1. Step 1: Obtain Official Decoder Script
Official GitHub decoder:
https://github.com/Milesight-IoT/SensorDecoders/blob/main/em-series/em300-sld/em300-sld-decoder.js
ThinkLink supports ChirpStack format without structural modification.
2. Step 2: Create a New Data Model
In ThinkLink:
Model Management → Create New Model
- Enter name, tags, description
- Select code format: ChirpStack
- Paste full decoder script
- Save
3. Step 3: Configure Data Fields
Important: Field identifiers must match decoder return keys exactly.
Recommended fields:
- water_leak_status
- battery_voltage
- temperature
- signal_quality
Save configuration.
4. Bind Model to Device
Device Management → Select device → Bind data model → Save
Parsed data will be displayed in real time.
Advanced: Data Enhancement & Logic Processing
You may extend decoder logic to implement:
- Unit conversion
- Threshold alarm flags
- Signal quality grading
- Gateway RSSI injection
- Device online state modeling This enables intelligent structured IoT modeling.
TKL + EB Solution
ThinkLink combined with EdgeBus enables:
Hardware-first deployment
Remote protocol adaptation
Scalable integration path
Ideal for legacy device LoRaWAN upgrades.
Experience ThinkLink online:
https://thinklink.manthink.cn
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