A dynamic control method and system for shrimp freezing process

By using a knowledge graph generated through a lightweight instance segmentation network and a knowledge distillation algorithm, combined with a neural symbolic reasoning system, the shrimp freezing process is dynamically controlled. This solves the problems of slow response speed and poor adaptability in existing technologies, achieving efficient and interpretable shrimp freezing control, and improving freezing quality and system adaptability.

CN121168672BActive Publication Date: 2026-04-10PANJIN MEIRI GRP CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
PANJIN MEIRI GRP CO LTD
Filing Date
2025-11-19
Publication Date
2026-04-10

AI Technical Summary

Technical Problem

Existing technologies for shrimp freezing suffer from slow response speed, lack of interpretability in decision-making, limited computing resources at edge nodes, and poor system adaptability, resulting in low control precision and difficulty in quality protection during the freezing of damaged shrimp.

Method used

A lightweight instance segmentation network is used to extract damage feature vectors, which are then combined with a knowledge distillation algorithm to generate a lightweight knowledge graph. This graph is deployed on edge computing nodes and dynamically controlled through a neural symbolic reasoning system to generate differentiated freezing control parameters. Finally, it is optimized online through a differentiable reasoning mechanism.

Benefits of technology

It achieves millisecond-level decision-making speed, provides interpretable decision-making basis, enables differentiated control for different damage types and severity, and improves freezing quality and system adaptability.

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Abstract

The present application relates to the technical field of aquatic product frozen processing, and discloses a prawn freezing process dynamic regulation method and system, wherein the method comprises the following steps: extracting a damage feature vector through a lightweight instance segmentation network; generating a lightweight knowledge graph based on knowledge distillation; converting the damage feature into a symbolic representation and performing neural symbolic reasoning; calculating a quality degradation risk index and generating differentiated control parameters; collecting feedback data and updating the knowledge graph weight. The present application solves the technical problems of slow response speed and poor interpretability of damaged prawns in the freezing process, and realizes millisecond-level real-time decision-making and precise control.
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Citation Information

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