AI Weighing Reliability Detection Without Extra Sensors
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Solution Overview
Problem
Conventional weighing systems struggle to detect hidden factors interfering with weighing accuracy and cannot determine the reliability of displayed weighing data, leading to potential inaccuracies and increased costs due to additional sensors.
Innovation Solution
A method for determining the reliability of a weighing result in a weighing system, which involves providing event and weighing data, storing them in a database, training an AI classification algorithm, and using the trained model to assess the reliability of the weighing result in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If additional sensors and signal detection technology are used to detect anomalies, then the reliability of weighing result determination is improved, but the device complexity and cost increase
Solution Approach 1:
The weighing system uses its own existing sensor data and operational parameters to detect anomalies and determine reliability, without requiring additional external sensors. The system serves itself by analyzing its own operational data through AI algorithms to identify hidden factors affecting weighing accuracy.
Solution Approach 2:
The patent replaces physical additional sensors with an AI-based software algorithm that processes existing sensor data. This substitution eliminates the need for extra hardware components while achieving anomaly detection through intelligent data analysis of the weighing system's operational parameters.
2Measurement precision
If additional sensors are added to detect anomalies, then the measurement precision is improved, but the cost increases
Solution Approach 1:
Instead of using additional physical sensors, the system creates a virtual model of the weighing system's operational state by copying and analyzing existing sensor data through AI algorithms. This digital twin approach enables anomaly detection without requiring duplicate or additional physical sensing hardware.
Solution Approach 2:
The patent substitutes mechanical sensor hardware with an AI-based software solution that achieves the same measurement precision by intelligently analyzing existing sensor data to detect anomalies and hidden factors affecting weighing accuracy.
3Device complexity
If conventional signal detection and software algorithms are used, then the device complexity is reduced, but the reliability of detecting hidden factors is insufficient
Solution Approach 1:
The patent transforms the approach by changing from conventional signal detection parameters to AI-based pattern recognition parameters. The system analyzes multiple operational parameters simultaneously using machine learning algorithms to identify hidden factors and anomaly patterns that conventional methods cannot detect, significantly improving reliability while maintaining acceptable device complexity.
Data Source
AI summary
A method for determining the reliability of a weighing result, a computer-readable medium, and a related system are disclosed. The method for determining the reliability of a weighing result includes: using an AI classification algorithm trained by data including event data related to a weight system and weighing data obtained by a sensor thereof, and making an inference about a weighing result, including providing latest data obtained by the same weighting system or a weigh system of a same kind to the AI classification algorithm, including latest event data and latest weighing data representing the weighing result. The method, the computer-readable medium, and the system disclosed herewith determine whether a weighing system is operating normally and whether displayed data is reliable.