Ontology library based transportation big data semantic application service method

Abstract

The technical solutions of the present invention are to provide an ontology library based transportation big data semantic application service method, and are characterized by comprising steps of: step 1, establishing an ontology library based transportation big data platform, wherein the transportation big data platform comprises a traffic data ontology layer, a transportation network model, a data ETL layer, a big data cluster layer, a data access service layer, an application service logic layer, a big data storage layer, and a logic design of a semantic access interface layer module; and step 2, performing data access based on semantic logic. The ontology library based transportation big data semantic application service architecture method provided by the present invention aims is to resolve ontological organization and intelligent service of multi-source heterogeneous data in an integrated environment of HADOOP/relational database, and especially a key technical problem that in a future large data open service environment, people can retrieve accurate data resources and corresponding services through voice and text.

Description

technical field [0001] The present invention relates to a traffic big data semantic application service method based on an ontology database. Aiming at the problems of association organization, integrated management and intelligent service of multi-source heterogeneous urban traffic data, a big data semantic service architecture design and solution based on traffic data ontology is proposed. The service process supports the public to obtain data queries, services and products through voice commands and keywords in natural language, and belongs to the field of intelligent transportation. Background technique [0002] After years of development in the construction of urban traffic informatization, a large amount of data resources have been accumulated, and these data resources are scattered in various industry departments and platform systems, which have played a huge role in informatization services. However, with the rapid growth of data scale, especially the open requiremen...

AI Technical Summary

Problems solved by technology

[0003] (1) Data storage isolation: The traffic data collected by various testing equipment are stored in independent data tables, and the data structure is also different. The data collected in different periods of engineering construction has not been unified and integrated. Extremely complicated, resulting in low efficiency of query data, multi-source correlation and in-depth application are extremely difficult

[0004] (2) Separation of data and source: through the storage mode of the relational table, the data is separated from the source, and the relationship between the data itself and the device and the location of the road network are not clear. As a result, the algorithm application and association analysis work needs to be carried out more Secondary configuration, resulting in duplication of labor

[0005] (3) Data storage is not standardized: the format of similar data stored in different departments or platforms is not uniform, and there are inconsistencies in the process of data network transmission, which makes it very difficult to use remote data online, and it is difficult to support large-scale multi-source heterogeneous data in the future. Network application of structured data

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