Analysis and prediction system based on attached pre-chain type data

Abstract

The invention relates to a data analysis and prediction system based on an attached pre-chain. The system comprises a decision item analysis module, an intelligent prediction management module, a data model management module, an attached pre-chain management module and a multi-dimensional data environment model analysis management module. The decision item analysis module is linked with a corresponding analyzed data source in a parameter configuration mode, and decision data, a first-class data model and a second-class data model are obtained by applying the configuration mode; the intelligent prediction management module is responsible for obtaining analysis conclusion conditions and analysis conclusions; the data model management module establishes a corresponding data model and a decision model; the attached and pre-chain management module establishes a corresponding attached model, a pre-model and a chain model; and the multi-dimensional data environment model analysis management performs data model environment comprehensive simulation to form an analysis result. According to the method, the repeatable, efficient and convenient calculation capability of a machine is exerted, an experiment scene is set, single calculation of data analysis and prediction is separated, and data analysis and prediction are carried out more effectively, conveniently and accurately.

Description

technical field [0001] The invention relates to data analysis and data prediction technology, in particular to a data analysis and prediction system based on attached pre-chain. Background technique [0002] At present, data analysis and prediction are mainly carried out by comparing personnel experience and historical data, establishing personnel experience functions or parameters, and establishing historical data comparison functions and parameters to predict future data. Personnel must be very clear about industry conditions and company conditions in order to better analyze and predict data. Since the conditions of each industry and company are different, it is difficult to use them repeatedly and the reproducibility is poor. Once relevant forecasters leave the industry or enter other companies, it is difficult for the accumulated experience to be perfectly replicated and reused. The traditional method is to establish a calculation function based on the comparison of his...

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Problems solved by technology

This kind of forecasting method is often inconsistent with the actual situation and cannot achieve the forecasting effect.

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