Method of realizing automatic grading of tax service halls based on big data mining algorithm

Abstract

The invention discloses a method of realizing automatic grading of tax service halls based on a big data mining algorithm. The method includes the steps of S1, in response to input data of a user, determining an indicator set required for automatic grading of tax service halls; S2, according to each indicator in the indicator set, performing data cleaning and transformation for acquired original data after the corresponding original data acquisition for a plurality of tax service halls; S3, calculating the weight of each indicator through a hierarchical analysis and processing method to construct a KPI algorithm model, and calculating an output KPI score of each tax service hall; and S4, classifying the plurality of tax service halls using K-means clustering algorithm according to the calculated KPI scores. The method of the invention can realize automatic grading of the tax service halls in a scientific and objective manner, has the characteristics of high stability, high data processing speed and high classification efficiency, and can be applied in the industry of weighting instruments.

Description

technical field [0001] The invention relates to the field of data processing, in particular to a method for realizing automatic grading of tax service halls based on big data mining algorithms. Background technique [0002] Glossary: [0003] KPI: Key Performance Indicator, key performance indicators; [0004] K-means clustering: a clustering algorithm, which is a typical representative of the prototype-based objective function clustering method. It is a certain distance from the data point to the prototype as the optimized objective function, and is obtained by using the method of finding the extreme value of the function. Adjustment rules for iterative operations; [0005] AHP method: Analytic Hierarchy Processing Method, the full name of AHP is Analytic Hierarchy Process, which is a decision-making method that decomposes the elements related to decision-making into goals, criteria, plans, etc., and conducts qualitative and quantitative analysis on this basis; [0006] ...

AI Technical Summary

Problems solved by technology

In general, the current classification method consumes a lot of manpower in the process of manual statistics and decision-making. At the same time, the classification process is mainly based on human subjective will, resulting in time-consuming and labor-intensive output of classification results, which is not objective enough. It is not scientific enough, and there is a situation where there is no unified standard in various cities and provinces, and the inability to benchmark across the province

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