Aquaculture monitoring method and system based on multi-modal sensing technology

By using three-dimensional mesh generation and comprehensive risk index calculation based on multimodal sensing technology, the problems of inaccurate pollution source location and delayed early warning in traditional aquaculture monitoring have been solved. This has enabled precise location and risk prediction of water quality gradient changes, providing accurate decision-making basis to reduce aquaculture losses.

CN122264541APending Publication Date: 2026-06-23SHAOXING UNIV YUANPEI COLLEGE
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
SHAOXING UNIV YUANPEI COLLEGE
Filing Date
2026-03-26
Publication Date
2026-06-23

AI Technical Summary

Technical Problem

Traditional aquaculture monitoring methods rely on single-parameter thresholds for early warning, which cannot accurately locate pollution sources, ignore the dynamic interaction effects between parameters, and lack comprehensive risk assessment and graded early warning based on multi-dimensional parameter fusion. This results in strong early warning lag and makes it difficult to provide accurate decision-making basis.

Method used

Based on multimodal sensing technology, this method uses three-dimensional mesh partitioning and adjacency matrix construction, combined with information entropy, dilated causal convolution and Mahalanobis distance, to dynamically allocate parameter weights and calculate a comprehensive risk index for risk classification and early warning.

Benefits of technology

It enables precise location of water quality gradient changes, improves the sensitivity of response to sudden pollution and the ability to predict risks, and provides accurate decision-making basis to reduce aquaculture losses.

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Abstract

The application provides an aquaculture monitoring method and system based on a multi-modal sensing technology, relates to the technical field of aquaculture monitoring methods, and comprises the following steps: calculating a comprehensive water quality characteristic value of each grid node, multiplying and aggregating an adjacency matrix and a characteristic matrix, simultaneously processing the first characteristic matrix by using an expanded causal convolution to obtain a space-time characteristic matrix of each time step; calculating a comprehensive risk index by using a weighted fusion method, and classifying and warning the comprehensive risk index. High-resolution spatial modeling is realized by grid division, local differences of parameters such as dissolved oxygen, temperature and ammonia concentration in different areas of the pool bottom are accurately captured, the spatial correlation between grid nodes is dynamically constructed based on the adjacency matrix, the space-time dependence relationship is simultaneously modeled by combining the adjacency matrix multiplication aggregation and the expanded causal convolution, and errors caused by spatial discretization in traditional methods are avoided.
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Citation Information

Patent Citations

  • Aquaculture monitoring method and system based on multi-mode sensing technology

    CN119534781A