Logical deduction method and system based on index direction

Abstract

The invention discloses a logic deduction method based on index direction, and the method comprises the following steps: employing a graphic configuration tool, carrying out index definition and direction connecting in a deduction graphical region, and building the binding relation between an index and data; obtaining a binding data source through a data loading engine; carrying out the call of a filter through a condition configuration tool to screen the data bound with the index, and carrying out the preposed binding of a current index and a correlated index; carrying out the analysis and reading of the data of the index and the data of the correlated index through the call of a condition analysis engine, generating an index result, and displaying the index result to a client. The method finally achieves the visual and effective logic deduction among the indexes, completes an analysis target, assists the information analysis and decision making, and digs out the value of data.

Description

technical field [0001] The invention relates to the field of information processing, in particular to a logical deduction method and system based on index pointing. Background technique [0002] With the wave of big data developed in recent years, enterprises and government units have accumulated a large amount of data, including their own business data and a wide range of social resources, and continue to expand data sources, but so far, the utilization rate of data mining is still low. Relatively low, especially in the field of public security. [0003] On the one hand, most of the current analysis applications train models based on business needs, and extract the same-sex needs. Once there are individual needs, due to the long R&D cycle and high cost, it will consume a lot of labor costs, and these needs will likely be eliminated. Filtering out, this has also led to many good analysis models and methods that have not been practiced and popularized, and cannot better supp...

AI Technical Summary

Problems solved by technology

[0003] On the one hand, most of the current analysis applications train models based on business needs, and extract the same-sex needs. Once there are individual needs, due to the long R&D cycle and high cost, it will consume a lot of labor costs, and these needs will likely be eliminated. Filtering out, this has also led to many good analysis models and methods that have not been practiced and popularized, and cannot better support actual combat, which also discourages the enthusiasm of some people;

[0004] On the other hand, the current application level of analysts is relatively single, and most of them have no technical background. For slightly more complex advanced applications, such as data modeling and other more complex functions, they cannot be used satisfactorily, and the demand is constantly occurring. The thinking of analysts is also constantly changing, and the existing fixed analysis mode has been difficult to meet the growing analysis needs in the future;

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