Construction and application of knowledge mapping of carbon emission governance technologies for coal mining and utilization - Ying WANG; Liya WANG; Fei MA; Yang YANG; Zishuai ZU (Meitan kexue jishu)
Coal is the main force of carbon reduction in energy consumption, and the carbon emissions generated in the process of coal exploitation and utilization account for about 60%−70% of the total national carbon emissions, which is the key to accomplishing the carbon reduction task in China. The construction and application of knowledge mapping of coal mining and utilization carbon emission management technology focuses on coal mining and utilization carbon emission management technology, systematically sorts out the knowledge of related management technology, and constructs knowledge mapping on the basis of which, to excavate the intrinsic connection, applicable conditions, implementation effect and emission reduction path of different technologies, to provide support for the relevant personnel to obtain the cutting-edge knowledge in the field of carbon emission management technology, and to push forward the transition of coal industry to the green and low-carbon direction. Transformation in the direction of green and low-carbon. First, we extensively collect professional books, terminology dictionaries, authoritative research reports, core journals on China Knowledge Network, and various standards and norms related to coal emission reduction technologies, and construct a conceptual knowledge model of coal mining and utilization of carbon emission management technologies by adopting a hybrid construction method of bottom-up and top-down; second, we use the BIO annotation strategy and apply the BERT+CRF (Bidirectional Encoder Representations from Transformer Representations) method to construct a conceptual knowledge model of coal mining and utilization of carbon emission management technologies. Encoder Representations from Transformers & Conditional Random Fields) model to recognize the entities in this domain; third, on the basis of entity recognition, the BiLSTM–Attention model is applied to further mine the relationships between entities and realize relationship extraction; fourth, entity The fourth is to use entity disambiguation and co-reference disambiguation techniques for knowledge fusion, eliminating contradictions and redundant information in the data; the fifth is to store the entities and relationships through the Neo4j graph database, based on the above structured methods and models, thus completing the construction of the knowledge map of the field of coal mining and utilization of carbon emission management technology. A conceptual model of knowledge in the field of coal mining and utilization carbon emission management technology covering 4 major categories of emission characteristics, mining methods, utilization methods and carbon reduction technologies is constructed, and the knowledge concepts of these 4 major categories are subdivided into 12 subclasses and 30 subclasses, forming a complete conceptual classification system. Ten types of named entities and six kinds of relationships are defined, and based on the proposed knowledge graph construction combination method and innovation model, 12 631 nodes and 32 209 inter-entity relationships are extracted, which reveals the complex association between carbon emission technologies and emission characteristics, mining methods, utilization methods, and based on the constructed knowledge graph in the field of coal mining and utilization of carbon emission governance technology, it can support the mining enterprises to select the appropriate carbon reduction technology path. The knowledge graph in the field of carbon emission management technology has been constructed to support mining enterprises in selecting appropriate carbon reduction technology paths. With the expansion of low-carbon development scenarios in the coal industry, the accumulation of data, and the development of artificial intelligence and big models, this study will optimize the construction method of the atlas on the basis of multimodal data fusion, expand the application scope of the atlas, and improve the accuracy of the recommendation of technology paths.