Multi-Sensor Calibration via Group Statistical Correction
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Solution Overview
Problem
Existing farm management systems require frequent and labor-intensive calibration of multiple sensors due to wear, varying usage frequencies, and differing environmental conditions, making it inefficient to maintain accurate animal data.
Innovation Solution
A method that involves obtaining measurements from multiple sensors for an individual animal, calculating relations between these measurements to determine correction parameters, and using these parameters to harmonize the output signals of all sensors, allowing for efficient calibration without individual sensor recalibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If each sensor is calibrated individually and frequently, then measurement precision is maintained, but labor intensity and time consumption increase significantly
Solution Approach 1:
The patent combines multiple individual sensor calibrations into a single group calibration process. By collecting measurements from multiple sensors simultaneously and performing unified statistical analysis, the system calibrates N sensors in one operation rather than N separate operations, directly reducing calibration time while maintaining precision through group-wise statistical correction
Solution Approach 2:
The system uses the sensors' own measurements and the natural variability in animal visits to self-calibrate. By leveraging the existing measurement data and statistical relationships between sensors, the calibration process requires minimal external intervention, automatically computing correction factors from the sensors' operational data
2Measurement precision
If each sensor is calibrated individually, then calibration accuracy is maintained, but device complexity and operational effort increase
Solution Approach 1:
The patent merges N individual calibration operations into a single group calibration process. The system collects measurements from multiple sensors and performs unified statistical analysis, reducing operational complexity from N separate calibration tasks to one integrated calibration operation while preserving accuracy through group-wise correction factor computation
Solution Approach 2:
The calibration system automatically computes correction factors using the sensors' own measurement data and statistical relationships. The process leverages natural animal visit patterns and measurement variability to self-calibrate without requiring manual intervention for each sensor, significantly easing operational burden
3Measurement precision
If sensors are calibrated frequently, then data accuracy is maintained, but productivity of the calibration process decreases
Solution Approach 1:
The patent combines multiple sensor calibrations into a single efficient group calibration process. By processing measurements from N sensors simultaneously and computing correction factors in one operation, the system achieves frequent calibration with high productivity, transforming what would be N separate time-consuming tasks into one efficient batch operation
Solution Approach 2:
The system automatically performs calibration using existing measurement data and statistical relationships between sensors. This self-calibration capability enables frequent updates of correction factors without requiring manual intervention, significantly improving calibration productivity while maintaining data accuracy through continuous statistical monitoring
Data Source
AI summary
A method of calibrating a sensors in a system for obtaining animal data from animals is described. The sensors are configured to obtain measurements of an animal related parameter during arbitrary visits by the animal to the sensors. For at least one of the animals, a first measurement associated with a first sensor of the sensors is obtained, and calculating one or more relations between the first measurement and one or more second measurements associated with the respective animal. Each of the second measurements is obtained using a further sensor, so as to obtain at least one representative relation for each combination of the first sensor and each one of the further sensors. The system calculates, based on the at least one representative relation, a correction factor associated with at least one sensor of the plurality of sensors.