Method for reverse tracing of pollution source of oil spill based on knowledge graph and space-time big data

By constructing a multi-source heterogeneous dataset with unified spatiotemporal benchmarks and a generative adversarial network, the problems of causal attribution and hidden behavior identification in the source tracing of submerged oil pollution were solved, and high-precision pollution source tracing was achieved.

CN122364569APending Publication Date: 2026-07-10CHINA WATERBORNE TRANSPORT RES INST +1
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
CHINA WATERBORNE TRANSPORT RES INST
Filing Date
2026-04-15
Publication Date
2026-07-10

AI Technical Summary

Technical Problem

Existing technologies for tracing the source of submerged oil pollution rely on statistical correlation analysis, which fails to achieve causal attribution. Furthermore, they lack the ability to adaptively identify and counteract dynamic, concealed behaviors, leading to misjudgments and the inability to identify new evasion strategies.

Method used

By collecting multi-source heterogeneous spatiotemporal data, a fused spatiotemporal dataset is constructed and transformed to the same geographic coordinate system and UTC time base, forming a multi-source heterogeneous spatiotemporal data set with a unified spatiotemporal base. A spatiotemporal backtracking probability field is generated, and a high-confidence hidden behavior pattern is identified using generative adversarial networks. The behavior-enhanced knowledge graph is then used for iterative optimization, ultimately generating a converged source-tracing association knowledge graph and a focused spatiotemporal source-tracing probability field.

Benefits of technology

It enables precise, reliable, and interpretable intelligent tracing of submerged oil pollution sources, can identify high-confidence concealed behavior patterns, improve the accuracy and reliability of source tracing, and reduce the false judgment rate.

✦ Generated by Eureka AI based on patent content.

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Abstract

This invention discloses a method for reverse tracing of submerged oil pollution sources based on knowledge graphs and spatiotemporal big data, relating to the field of environmental monitoring technology. The method includes: collecting multi-source heterogeneous spatiotemporal data and aligning and fusing it to form a fused spatiotemporal dataset; constructing a dynamic spatiotemporal association knowledge graph centered on pollution events based on the fused spatiotemporal dataset to form an initial source-tracing association knowledge subgraph; generating a spatiotemporal backtracking probability field based on a reverse spatiotemporal probability field simulation using multiple hypothesis sets; and inputting the initial source-tracing association knowledge subgraph into a hidden behavior discrimination model trained on a generative adversarial network to identify high-confidence hidden behavior patterns. This invention uses a counterfactual causal inference framework to evaluate and rank the causal effects of associated entities in the converged source-tracing association knowledge graph, obtaining reverse tracing conclusions, thereby achieving accurate, reliable, and interpretable intelligent tracing of submerged oil pollution sources.
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