one thousandth of a degree, enhanced by a specially designed Kalman filter
Multiple sensoring
The first multi-sensor device in the world integrating inclination and soil moisture sensoring
AI machine learing
Based on the senor data, Jing users can build their own data set, training set to set up a prediction model
For the first time , one device can measure the causes and the consequences
Moisture at different depths
Temperature at different depths
Real-time inclination sensoring
Rainfall penetration in the soil
Jing : More than a hardware
Kalman fliter
We have specially designed a digital Kalman fliter to increase the stability of the sensor data and to produce estimates of unknown inclination variables that tend to be more accurate by estimating a joint probability distribution over the variables for each timeframe
Kalman fliter
We have specially designed a digital Kalman fliter to increase the stability of the sensor data and to produce estimates of unknown inclination variables that tend to be more accurate by estimating a joint probability distribution over the variables for each timeframe
Open data API
We provide a large amount of historical and real-time
meteorological data for building a comprehensive
data base for the safety assessment
or early warning modeling
Machine learning by TensorFlow
AI technology on the software side (deep learning) for
automatic modeling on landslide potential index or safe index
leveraging on the data provided by Jing
Machine learning by TensorFlow
AI technology on the software side (deep learning) for
automatic modeling on landslide potential index or safe index
leveraging on the data provided by Jing