A method and system for constructing a fishing multi-source data fusion

By employing spatiotemporal alignment and graph structure construction methods for fish catch data, the heterogeneity and noise issues of fish catch data were resolved. This enabled the orderly synthesis of multi-scale information and a comprehensive improvement in the dataset, thereby enhancing the accuracy and usability of fishery resource assessment.

CN121389012BActive Publication Date: 2026-06-19GUANGDONG OCEAN UNIVERSITY
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
GUANGDONG OCEAN UNIVERSITY
Filing Date
2025-10-31
Publication Date
2026-06-19

AI Technical Summary

⚠Technical Problem

Fish catch data comes from multiple heterogeneous sources, with significant differences in spatiotemporal scale, resolution, and semantics, leading to information fragmentation, inconsistency, and noise. Direct correlation may amplify the interference of low-quality data and weaken the reliability of the overall dataset.

⚗Method used

By acquiring production, fishing vessel trajectories, environmental and social data of the target area, spatiotemporal alignment is performed based on a preset sea area grid and time window. Coarse and fine grids are divided, a graph structure is constructed, node correlation and structural similarity are calculated, and weighted fusion is performed using an attention module to obtain a fused structure graph.

🎯Benefits of technology

It enables the orderly synthesis of multi-scale information, improves the comprehensiveness and usability of catch data, enhances the accuracy and usability of fishery resource assessment, reduces the impact of noise, and ensures the traceability and completeness of data attributes.

✦ Generated by Eureka AI based on patent content.

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Abstract

This invention discloses a method and system for constructing multi-source fish catch data fusion, belonging to the field of data fusion technology. The invention performs spatiotemporal alignment and preprocessing on heterogeneous data such as yield, fishing vessels, surveys, environment, and socioeconomic data, and then constructs a graph model containing multiple types of nodes and multiple relational edges. This allows for weighted fusion of two graphs based on the fusion weights to obtain a fused structure graph, enabling the orderly synthesis of multi-scale information and improving the completeness and robustness of the graph representation. Furthermore, a dual attention mechanism at the node and edge layers is introduced to achieve adaptive weighted fusion of multi-source information, generating a highly complete sea area-time fused feature dataset. The fused structure graph is then input into a fusion network containing node and edge layer attention modules for end-to-end fusion, thereby obtaining more accurate and comprehensive multi-source fused feature data of fish catch, improving the accuracy and usability of fishery resource assessment.
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