A cloud platform-based dairy product traceability management system

By combining multi-source heterogeneous data aggregation and Riemann curvature tensor recognition technology with deep integration of dynamic entropy increase index and ERP system, the problems of hidden damage identification and high storage costs during dairy transportation have been solved, achieving precise control of dairy quality and automated inventory control.

CN121836757BActive Publication Date: 2026-05-29SHAANXI YOUAIBEITE DAIRY CO LTD
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Patent Information

Application Number
CN202610308746.1
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2026-03-13
Publication Date
2026-05-29
Estimated Expiration
2046-03-13

AI Technical Summary

Technical Problem

Existing technologies struggle to identify hidden cumulative damage during dairy product transportation, and the high cost of storing massive amounts of raw logs results in slow response times. Furthermore, the lack of deep integration with enterprise business systems prevents automated inventory control.

Method used

A multi-source heterogeneous data aggregation unit is used for data cleaning and synchronization. The Riemann curvature tensor is used to identify hidden damage that has been restored by temperature but has lost mass. The storage cost is reduced by manifold mapping technology. The quality early warning signal is generated by combining dynamic entropy increase index. It is also deeply coupled with the enterprise ERP system to realize automated inventory blocking.

Benefits of technology

It enables precise control over dairy product quality, reduces storage costs, improves response speed, eliminates the lag in human decision-making, and enhances the risk control capabilities of food safety management.

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Abstract

The present application relates to the technical field of cold chain logistics Internet of Things and intelligent analysis of food safety data, in particular to a dairy product traceability management system based on a cloud platform, comprising: a multi-source heterogeneous data aggregation unit for generating an environmental state vector sequence; a quality manifold space mapping unit for fitting the discrete state vectors into actual transportation trajectory curves using a manifold projection algorithm; a trajectory curvature deviation analysis unit for extracting a Riemann curvature tensor feature and generating a damage scalar flow; a dynamic entropy increase index evaluation unit for generating a dynamic entropy increase index, a graded quality warning signal and a dynamic shelf life suggestion value; and an adaptive risk blocking and feedback unit for feeding the abnormal actual transportation trajectory curve data as negative samples to the quality manifold space mapping unit. The present application solves the defect that traditional discrete threshold alarms cannot identify process cumulative damage, and realizes precise control of dairy product quality.
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