A method and system for constructing a large language model enhanced emission trading knowledge graph

By using a large language model enhancement method, combined with domain-adaptive hints and multi-strategy verification, a high-quality pollution rights trading knowledge graph was constructed, which solves the problems of insufficient adaptability and intelligent interaction capabilities in existing technologies, and realizes efficient knowledge graph construction and intelligent question answering capabilities.

CN122154879APending Publication Date: 2026-06-05CHINA JAPAN FRIENDSHIP ENVIRONMENTAL PROTECTION CENT
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
CHINA JAPAN FRIENDSHIP ENVIRONMENTAL PROTECTION CENT
Filing Date
2026-03-24
Publication Date
2026-06-05

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Abstract

The application discloses a kind of big language model enhanced emission trading knowledge graph construction method and system, it is related to machine learning field.The present application obtains the unstructured text data in the field of emission trading, carries out document analysis and text segmentation to text data, obtains standardized text fragment;Based on the field of emission trading adaptability prompt engineering guides big language model, carries out entity recognition and relationship extraction to the text fragment, obtains initial knowledge triple and processing, obtains high-quality knowledge triple;Text fragment is converted and constructs structured triple with high-quality knowledge triple and semantic vector fusion's bimodal knowledge representation;The bimodal knowledge representation is stored to graph database, and constructs the field of emission trading knowledge graph;Based on double-path retrieval, carry out retrieval and knowledge traceability to knowledge graph, realize the field of emission trading intelligent question and answer and knowledge application.The present application realizes the efficient conversion from unstructured data to structured knowledge.
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