Digital Twin Refrigeration Compartment Monitoring for Perishable Quality
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Solution Overview
Problem
Current methods for evaluating the condition of perishable goods during transportation are inefficient, relying on manual sampling and temperature monitoring, which can miss issues with non-sampled items and provide misleading results, and are operationally impractical for extensive evaluations.
Innovation Solution
A digital twin-based system that uses real-time temperature and sensor data to simulate refrigeration compartments, evaluate the quality condition of perishable items, and predict potential impacts, providing recommendations through a user interface based on predefined parameters and quality metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual sampling and temperature monitoring are used to evaluate perishable goods, then the evaluation process is simple to implement, but the measurement precision and reliability are insufficient due to arbitrary sampling
Solution Approach 1:
The patent creates a digital twin (virtual copy) of the refrigeration compartment that replicates the physical environment and simulates temperature distribution, airflow patterns, and product condition. This digital copy enables comprehensive evaluation of all products without physical sampling, resolving the contradiction between measurement precision and system complexity by using simulation rather than extensive physical measurement.
Solution Approach 2:
The patent replaces manual physical inspection and sampling with automated simulation-based evaluation. The digital twin model substitutes mechanical sampling procedures with computational modeling that predicts product condition based on temperature data, airflow simulation, and product characteristics, thereby improving measurement precision while maintaining manageable system complexity.
2Productivity
If arbitrary samples are selected for manual evaluation, then the operation is simple, but the productivity is low and comprehensive quality assessment is missed
Solution Approach 1:
The digital twin system performs multiple evaluation functions simultaneously: temperature distribution analysis, airflow pattern assessment, product quality prediction, and risk identification. This multi-functional approach increases productivity by consolidating multiple evaluation tasks into a single simulation system while preventing information loss through comprehensive assessment of all products.
Solution Approach 2:
The system performs preliminary simulation and prediction of product quality conditions before actual delivery or inspection. By evaluating potential quality issues in advance through the digital twin, the system identifies problems that would be missed by manual sampling, thereby improving both productivity and information completeness.
3Measurement precision
If extensive manual inspections are performed to evaluate all perishable goods, then the measurement precision improves, but the ease of operation and time consumption worsen
Solution Approach 1:
The digital twin system performs self-evaluation by automatically simulating and assessing product quality conditions based on input data from sensors and product specifications. The system serves itself by generating comprehensive quality reports without requiring manual intervention for each evaluation, thereby maintaining high measurement precision while improving ease of operation.
Solution Approach 2:
The system continuously monitors temperature and environmental parameters, feeds this data into the digital twin model, and generates real-time quality assessments. This closed-loop feedback mechanism maintains high evaluation accuracy while automating the process, eliminating the need for complex manual inspection procedures.
Data Source
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AI summary
Embodiments of the invention describes a system and a method (300) for evaluating the condition of perishable goods inside a refrigeration container (103) of a vehicle (100), using a digital twin. According to an embodiment the method (300) includes performing (305) a simulation process to simulate at least one refrigeration compartment (103) by using a digital twin model that characterizes a variability of vehicle parameters, food parameters, and conditions related to at least a specific operation to a transport of the perishable goods and initial storage conditions. The method (300) includes periodically evaluating (307) a current quality condition of the perishable items based on a result of the simulation process and a plurality of predefined evaluation parameters and comparing (309) a result of evaluation with a threshold value associated with a current quality condition of the perishable items. The method (300) includes predicting (311) an impact on the current quality condition of the perishable items based on a result of comparison and provide a recommendation.