Asset big data intelligent analysis method

Abstract

Aiming at the defects in the prior art, the invention provides an asset big data intelligent analysis method which comprehensively considers dimensions involved in query requirements and distributes the dimensions to different routes according to query specific contents, thereby realizing quick and efficient query of the overall condition of asset business and ensuring flexibility. In order to achieve the purpose, the asset big data intelligent analysis method comprises the following steps: carrying out multi-dimensional cubic construction on an integrated data set subjected to ETL conversion;obtaining the index data meeting the query condition from the multi-dimensional data cube, and taking the index data as a response result of the query request. According to the method, the physical data model is mapped from the aspects of the asset field model and the concept model, so that business personnel can build and configure a big data multi-dimensional model in a self-service manner andperform optimization and query according to pure business understanding, and the problem that the business personnel are difficult to understand underlying data is avoided.

Description

technical field [0001] The invention relates to a product automatic classification method, in particular to an asset big data intelligent analysis method. Background technique [0002] After a long period of operation, the enterprise's business and asset systems have accumulated a large amount of structured and unstructured data, but these data only exist in various isolated asset information systems, and have not been fully utilized, and the value of the data has not been realized. Dig. In the process of analyzing big data of assets and assets, due to the large scale of transaction data and complex business relationships, databases in the prior art often have to perform comprehensive queries on multiple data tables, which on the one hand leads to low query efficiency on limited hardware resources , on the other hand, it cannot meet the flexible and comprehensive query requirements for multidimensional data. Therefore, a new data storage and analysis method and system are ...

AI Technical Summary

Problems solved by technology

[0006] However, in terms of function, this analysis method still responds to diverse query requirements in the form of temporary calculation, and only meets the query requirements of historical offline data in terms of pre-computing technology; in terms of ease of use, it is only for data developers and those who are more familiar with data Modelers, not friendly enough for business people