Abnormal data detection and modification method based on numerical value relevance model

Abstract

The invention discloses an abnormal data detection and modification method based on a numerical value relevance model. The method includes the following steps that first, abnormal data judgment conditions are defined by a display module and stored into a source indicator database; second, a data access layer traverses service indicator data in the source indicator database, judges whether the service indicator data meet the abnormal data judgment conditions or not and displays the service indicator data meeting the abnormal data judgment conditions in the display module, and meanwhile the third step is executed; third, the service indicator data meeting the abnormal data judgment conditions are modified by a service logic module, and the modified data are stored into the source indicator database according to needs. The abnormal data detection and modification method based on the numerical value relevance model greatly improves accuracy of service data of a power grid and accuracy for calculating statistical data groups, makes the statistical analysis result more reliable and provides favorable data support for enterprises in the respects of investment evaluation, benefit analysis and the like.

Description

technical field [0001] The invention relates to an abnormal data processing method, in particular to an abnormal data detection and correction method based on a numerical correlation model. Background technique [0002] With the comprehensive development of enterprise informatization, the dependence of enterprises on data is gradually increasing, and data information is increasingly becoming an important strategic resource for enterprises. The quality of data is directly related to the accuracy of information, and also affects the survival and development of enterprises. competitiveness. Under the background of smart grid promotion, power grid enterprises continue to establish and improve existing information systems based on their own business characteristics, which basically cover finance, marketing, production safety, collaborative office, human resources, materials, project management, comprehensive power, etc. The main business scope of the enterprise. At the same tim...

AI Technical Summary

Problems solved by technology

In the process of business data generation, due to the differences and loopholes in data collection technology, statistical caliber, personnel quality, management mechanism, etc., a considerable part of the business data does not meet the business needs in terms of accuracy, thus affecting the data analysis and data of the enterprise. serious negative impact on processing, investment decisions, etc.

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