Enterprise knowledge graph construction method and device

Abstract

The invention provides an enterprise knowledge graph construction method and device. The method comprises the following steps: acquiring structured Internet of Things service data of a plurality of enterprises; extracting structured Internet of Things business data knowledge of a plurality of enterprises to obtain graph data; obtaining a predefined graph framework model, and storing the graph datain the graph framework model to obtain an enterprise knowledge graph which is used for representing the Internet of Things business condition of each enterprise. By extracting structured Internet ofThings service data knowledge of a plurality of enterprises to obtain atlas data, the graph data is stored in the predefined graph framework model to obtain the enterprise knowledge graph, a large amount of data and complex and diverse association analysis can be processed, the specific situation of the Internet of Things service of each enterprise can be visually and stereoscopically reflected inthe form of the graph, and the analysis and management requirements of various role relationships of each enterprise are met.

Description

technical field [0001] The present invention relates to the technical field of information processing, in particular to a method and device for constructing an enterprise knowledge map. Background technique [0002] At present, in the face of complex and diverse enterprise-level association analysis of IoT big data business processing, there are problems of high coding cost and high performance overhead of task submission, and the statistical results of business processing are not intuitive and accurate enough for the specific description of the enterprise. [0003] The existing IoT big data analysis technology has the following defects: [0004] There are many types of Internet of Things billing data sources and networks, and the enterprise has a large amount of user information and needs to be updated in full. There are also other massive, heterogeneous, and dynamic large-scale data. When faced with the statistical analysis of enterprises, it is necessary to use big data ...

AI Technical Summary

Problems solved by technology

[0004] There are many types of Internet of Things billing data sources and networks, and the enterprise has a large amount of user information and needs to be updated in full. There are also other massive, heterogeneous, and dynamic large-scale data. When faced with the statistical analysis of enterprises, it is necessary to use big data Analysis technology requires repeated query of huge and complex related information and submission of analysis tasks, which affects the rational use of platform resources

[0005] It requires development and maintenance personnel to invest a lot of cost, frequently submit jobs to the big data analysis platform, and needs to recode every time, which consumes a lot of manpower and material resources

[0006] At present, when big data analysis technology conducts temporary statistical analysis on the enterprise level of the Internet of Things, the results generated by the associated information are generally simple traditional database storage methods or just a few strings of numbers, and the ability to describe the business scenario of the enterprise is far away. Insufficient, does not show the enterprise association intuitively and three-dimensionally, cannot cope with the complex needs in the actual business, and is difficult to clearly understand the user's intention

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