Abstract: The present invention discloses a method for identifying an optimal influence customer group in a marketing network based on a hypergraph threshold model. The method specifically comprises: step 1: establishing a hypergraph threshold model based on sales data first. Step 2: using the hypergraph threshold model to simulate spread of customer reputation in a marketing network, and setting a hyperedge threshold according to a specific scenario. Step 3: using an HCI-TM algorithm to screen out a most recommendable customer group for popularization, and using a hypergraph threshold rule to simulate spread of information among customers. The present invention proposes a multi-node collective influence measurement method for the hypergraph threshold model, designs the HCI-TM algorithm to efficiently select a maximum influence set, and applies the algorithm in the marketing network at the same time.

Abstract: The present invention provides a data fusion and reconstruction method for fine chemical industry safety production based on a virtual knowledge graph. In view of the characteristics of fine chemical industry safety production data, such as a large amount of structured data, a multi-source heterogeneous database and a strong sequential logic, the present invention innovatively proposes a method of using a virtual knowledge graph to complete the fusion and reconstruction of a traditional database for fine chemical industry. The present invention fuses static structured knowledge in the field of fine chemical industry with a real-time dynamic database for chemical industry safety production in the concept of ontologies for the first time to organize time series data in the form of entities. In addition, the mapping rules of the existing OBDA system are improved based on a data set of the present invention.

Abstract: The present invention belongs to the technical field of knowledge graph, and provides a knowledge graph construction method for an ethylene oxide derivatives production process. According to data types and characteristics, data sources of the ethylene oxide derivatives production process are sorted and divided into three types: structural data, unstructured data and other types of data. An ontology layer and a data layer of a knowledge graph are constructed by combining top-down and bottom-up methods. A data-driven incremental ontology modeling method is proposed to ensure the expandability of the knowledge graph. For structural knowledge extraction, the safety of original data storage is ensured by means of virtual knowledge graph, and a new mapping mechanism is proposed to realize data materialization. For unstructured knowledge extraction, an entity extraction task is realized on the basis of a BERT-BiLSTM-CRF named entity recognition model by integrating a pre-training language model BERT.