Metadata Management And Application In Logistics Data Fusion System

In recent years,with the increasingly intensive work of various logistics platforms,the logistics field has become a typical knowledge-intensive field.At the same time,due to the continuous improvement of the requirements of the logistics industry for data informatization,the upgrading of the platform class of the logistics industry is imminent.In order to meet the demand of data sharing among logistics enterprises,logistics data fusion system has become the current mainstream solution.However,from the perspective of the demand of intelligent logistics,the data processing in these data fusion systems at this stage generally has problems such as lack of semantics,relatively fragmented,no model reuse,and inadequate system.Therefore,it is very important for the development of logistics data fusion system and material flow field to choose a semantic data fusion method with stronger human-computer interaction to carry out relevant research on knowledge sharing in logistics field.This paper studies the metadata management and application in the logistics data fusion system based on the metadata reflecting the data content,quality,status and other information in the logistics data fusion system.Based on the research of metadata standards in the field of logistics at home and abroad,the logistics metadata standards are expanded from the semantic perspective,and the knowledge division and extraction of logistics scenarios are carried out using technologies such as ontology,knowledge atlas and virtual knowledge atlas.A three-level standard logistics knowledge system data fusion model guided by metadata standards in the multi-source heterogeneous logistics data fusion system is proposed and constructed,This model integrates metadata and ontology standards related to the logistics field at home and abroad,reveals the important idea of taking metadata standards as the core in the construction of semantic ontology,makes the knowledge ontology built in the logistics field realize semantic standardization,thus laying the foundation for knowledge sharing and interoperability,and provides standards for data fusion of data structure and semantic heterogeneity in the logistics data fusion system,so as to solve the existing data fragmentation Data fusion has no model reuse problem.In addition,combining the standard knowledge of logistics field provided by the data fusion model of the standard logistics knowledge system and the actual logistics scenario,this paper divides the metadata in the logistics data fusion system into system-level metadata and logistics domain level metadata for management.Through the technical application of database extraction and virtual knowledge map,the function is divided according to the role in the logistics industry,and the trusted generation of semantic fine-grained data units with data not separated from the local is realized,and encapsulated as an interface.So that the users of the logistics data fusion system can manage and apply the logistics metadata with semantics,obtain better use results,and solve the contradiction between data privacy and sharing in the logistics data fusion system in a way that the data is not separated from the local.