A system for monitoring and analyzing processing characteristics of early indica rice during storage of rice flour

By constructing a three-dimensional quality deterioration risk map and intelligent hierarchical sampling, combined with differential scanning calorimetry and covariate kriging algorithm, the problem of monitoring the degree of rice aging during the storage of early indica rice was solved, realizing precise management and decision support for rice noodle production.

CN121955313BActive Publication Date: 2026-06-19CHINA STORAGE GRAIN JIANGXI QUALITY INSPECTION CENT CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
CHINA STORAGE GRAIN JIANGXI QUALITY INSPECTION CENT CO LTD
Filing Date
2026-04-02
Publication Date
2026-06-19

AI Technical Summary

⚠Technical Problem

In rice noodle production, existing technologies cannot effectively monitor changes in the processing characteristics of early indica rice during storage, resulting in an inability to accurately assess the aging degree of the entire warehouse of rice, which may lead to misjudgment or economic losses.

⚗Method used

A three-dimensional quality deterioration risk map is constructed. Rice samples are obtained through intelligent stratified sampling, differential scanning calorimetry is performed, a thermodynamic dataset is generated, and the covariate kriging algorithm is used for data fusion and prediction to form aging degree prediction surface data and decision report.

🎯Benefits of technology

It enables precise monitoring of the aging degree of early indica rice during storage, avoids misjudgment, provides accurate warehouse management decision support, and reduces the risk of raw material misuse and economic losses.

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Abstract

This invention relates to the field of grain storage monitoring technology, specifically disclosing a system for monitoring and analyzing the processing characteristics of early indica rice for rice flour storage. The system generates a three-dimensional quality deterioration risk map based on storage environment data, and uses this map for stratified spatial sampling to obtain a set of samples with coordinate labels. Differential scanning calorimetry (DSC) is performed on the samples to obtain gelatinization enthalpy data, which is then integrated with the spatial coordinates and risk levels of the samples into a structured database. Furthermore, using spatial coordinates as the location variable, gelatinization enthalpy as the target variable, and risk as the auxiliary variable, a covariate kriging algorithm is employed to generate a continuous aging prediction surface covering the entire storage space and corresponding uncertainty assessment data. Finally, based on the prediction results, a three-dimensional visualized distribution map is constructed, overall storage statistical indicators are calculated, and high-confidence local deterioration areas are identified, generating a quality decision report. This invention effectively solves the monitoring distortion problem of traditional methods.
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