A food fast inspection information standardization pushing method and system

By constructing a multi-dimensional data network and machine learning model, and combining unique label codes with rapid testing reagent mapping, the required testing items are dynamically adjusted, solving the standardization problem of rapid food testing and achieving efficient and safe food testing.

CN121436631BActive Publication Date: 2026-07-10JIANGXI PROVINCIAL INST OF FOOD INSPECTION & TESTING (JIANGXI NAT FRUIT & VEGETABLE PROD & PROCESSED FOOD QUALITY SUPERVISION & INSPECTION CENT) +2
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
JIANGXI PROVINCIAL INST OF FOOD INSPECTION & TESTING (JIANGXI NAT FRUIT & VEGETABLE PROD & PROCESSED FOOD QUALITY SUPERVISION & INSPECTION CENT)
Filing Date
2025-09-17
Publication Date
2026-07-10

AI Technical Summary

Technical Problem

Existing technologies make it difficult to standardize rapid food testing, especially due to the wide variety of food types and the diversity and variability of the testing items, which leads to high technical requirements for rapid testing personnel in different regions and makes it difficult to standardize the process.

Method used

By constructing a multi-dimensional data collection and integration network, adopting a hybrid algorithm model of risk weight calculation and machine learning, dynamically adjusting the items to be tested, and using unique tag codes to push the items to be tested, combined with the mapping relationship of rapid test reagents, the standardization and efficiency of testing are ensured.

Benefits of technology

It has achieved standardization and efficiency in food testing in different regions, enabling it to keep up with changes in risks, take into account regional differences and special needs, and maximize food safety.

✦ Generated by Eureka AI based on patent content.

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Abstract

The application provides a food fast inspection information standardization pushing method and system, different regions of each food are screened and evaluated, the inspection items of different regions of each food are determined, and a first mapping relationship between the food information of different regions of each food and the corresponding inspection items is established, wherein a hybrid algorithm model of risk weight calculation and machine learning is fused to dynamically adjust the inspection items; when food detection is performed, the food information is acquired according to the unique label code of the food to be inspected, the inspection items are determined according to the food information and the first mapping relationship, and the inspection items are pushed, specifically, the method of dynamic evaluation through intelligent algorithm can ensure that the inspection items follow the risk changes, and can also take into account regional differences and special needs, thereby maximizing the guarantee of food safety while improving efficiency.
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