Method for normalizing financial data into evaluation data

Abstract

The invention relates to a standardization method for converting financial data into evaluation data. The method comprises the steps: carrying out the improvement of the data conversion matching degree through the data conditions in a machine self-learning importing process and a result after human intervention; multiple repeated import of data conversion can be realized; when the conversion condition is changed, the data conversion can be updated again only by adjusting and converting the data of the corresponding subject; the writing threshold of data conversion is reduced, the consistency of data during repeated import is ensured, and the data conversion efficiency and accuracy are improved.

Description

technical field [0001] The invention relates to a standardization method for converting financial data into evaluation data. Background technique [0002] Traditional software for converting financial data into evaluation data is based on the C / S architecture. Users need to install the corresponding software on their computers before they can use it. The conditions of use are inconvenient, and they cannot be operated without a common computer. In the previous appraisal work, many corporate financial statements received by appraisers were data derived from various financial software used by various companies, which required purely artificial analysis and splitting by appraisers for subsequent appraisals. [0003] The conversion of financial data into evaluation data is to achieve the function of matching and converting data from one expression form to another, but the existing data conversion methods have the following defects: data conversion can only be performed in pairs a...

AI Technical Summary

Problems solved by technology

[0003] The conversion of financial data into evaluation data is to achieve the function of matching and converting data from one expression form to another, but the existing data conversion methods have the following defects: data conversion can only be performed in pairs according to the templates of different financial software systems A targeted matching rule writing, to be compatible with most of the financial software templates on the market, it is necessary to write hundreds of different types of matching rules, the data conversion writing is complicated and inefficient; when the template changes and the conversion conditions change , it is necessary to re-calculate all fields of the data according to the new conversion conditions, which requires a lot of manpower and low data maintenance efficiency

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