A blockchain-based seafood supply chain traceability and risk early warning method

By combining blockchain, IoT and AI, a closed-loop system for traceability and risk warning of the seafood supply chain has been built, solving the problems of easy data tampering and delayed risk identification, and realizing transparent management and efficient risk prevention and control of the seafood supply chain.

CN122367495APending Publication Date: 2026-07-10福州海洋研究院
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
福州海洋研究院
Filing Date
2026-04-09
Publication Date
2026-07-10

AI Technical Summary

Technical Problem

The existing seafood supply chain suffers from data tampering, weak anti-counterfeiting capabilities, delayed risk identification, and low regulatory efficiency. It also lacks real-time risk warning capabilities. Traditional traceability systems cannot achieve reliable data storage and efficient traceability in high-frequency data scenarios.

Method used

By employing blockchain technology to achieve tamper-proof data storage, combined with real-time IoT sensing and artificial intelligence for anomaly identification and risk assessment, and triggering early warning strategies through smart contracts, a layered and phased closed-loop system for traceability and risk early warning is constructed, including data preprocessing, summary calculation, anomaly detection, risk scoring, and strategy optimization.

Benefits of technology

It has enabled transparent management of the entire seafood supply chain, improved risk control capabilities and traceability accuracy, reduced system maintenance costs, improved the real-time and accuracy of risk identification, and optimized supply chain operation efficiency.

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Abstract

This invention discloses a blockchain-based method for seafood supply chain traceability and risk warning, belonging to the field of seafood supply technology. The method collects data by deploying IoT devices at each node of the supply chain, and generates two-factor anti-counterfeiting labels containing origin and appearance information via an edge gateway. The system employs a hierarchical mechanism of storing key summaries on-chain and raw data off-chain to ensure data immutability. It dynamically monitors indicators such as temperature and path using a time-sliding window, performs risk scoring through an adaptive neuro-fuzzy inference model, and triggers smart contracts based on the scoring results to achieve tiered warnings and automatic handling. Finally, based on historical feedback, the risk model and cold chain strategy are adaptively optimized, forming a closed-loop management system from data collection and intelligent judgment to strategy adjustment, effectively improving the traceability credibility and risk warning capabilities of the seafood supply chain.
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