Meat Processing Line Control With Feedback-Based Outcome Prediction
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Solution Overview
Problem
Existing meat processing technologies fail to optimally utilize information from the processing line for dynamic improvement, particularly in predicting and optimizing output, leading to inefficiencies such as waste, downgrading, and mismatched production.
Innovation Solution
A computer-implemented method that obtains data elements from meat elements, predicts outcomes using mapping functions, determines processing instructions, measures physical properties, and updates these functions based on actual output to enhance prediction accuracy and optimize operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If mapping functions are used to predict meat processing outcomes, then productivity is improved through optimized processing instructions, but the accuracy of predictions deteriorates due to lack of feedback from actual measured outcomes
Solution Approach 1:
The system implements a feedback mechanism where actual measured physical properties of processed meat elements are fed back to update the mapping functions. This closed-loop approach allows the mapping functions to continuously improve their prediction accuracy by learning from real outcomes, resolving the contradiction between maintaining productivity through optimized instructions and improving prediction accuracy through feedback.
2Measurement precision
If mapping functions are updated continuously with measured data, then prediction accuracy is improved, but device complexity increases due to additional measurement and updating mechanisms
Solution Approach 1:
The system employs self-service principles where the mapping functions automatically update themselves using measured data from the processing line without requiring external intervention. The computer implementation automates the entire process of data collection, analysis, and function updating, reducing the need for complex manual management mechanisms while maintaining improved prediction accuracy.
3Measurement precision
If more data elements are obtained and measured for each meat element, then prediction accuracy is improved, but loss of time increases due to additional data collection and processing steps
Solution Approach 1:
The system performs preliminary actions by obtaining data elements about meat elements before they enter the processing line. This allows predictions to be made in advance, and the mapping functions to be updated with measured outcomes without adding time to the actual processing workflow. The data collection and prediction processes are decoupled from the critical processing path.
Data Source
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AI summary
There is provided in a first aspect of the present disclosure a computer-implemented method of controlling a meat processing line, such as a meat processing line in a slaughterhouse, transporting a plurality of meat elements to be processed by means of a conveyor. The method comprises the following steps: for each meat element of the plurality of meat elements to be processed, obtaining at least one data element relating to said meat element; based on the at least one obtained data element and based on at least one mapping function adapted for mapping data elements to outcome predictions, predicting at least one meat processing outcome; based on the at least one predicted meat processing outcome, determining instructions for processing said meat element; processing the meat element according to the instructions for processing said meat element, resulting in one or more processed meat elements; measuring at least one physical property of at least one element of the one or more processed meat elements, wherein the at least one physical property relates to the at least one mapping function; and updating the at least one mapping function, based on the measured at least one physical property.