Aluminum electrolytic capacitor purchase prediction method based on KNN algorithm of Mahalanobis distance

Abstract

The invention provides an aluminum electrolytic capacitor purchase prediction method based on a KNN algorithm of Mahalanobis distance, and the method is characterized in that the method comprises thesteps: 1, determining the key parameters of an aluminum electrolytic capacitor through the material analysis of the aluminum electrolytic capacitor, carrying out the preliminary extraction of the keyparameter items of the aluminum electrolytic capacitor through a frequent item set extraction method, through the parameter items appearing in the frequent item set, finally confirming the key parameter items of the aluminum electrolytic capacitor; 2, determining a parameter matching scheme according to a decomposition rule described by the material, and determining a usable product range; 3, providing purchase prediction of the product at the current user by adopting a predicted purchase method of a KNN algorithm based on Mahalanobis distance, and preliminarily screening a list of products which may be purchased for the user. According to the method, the functions of product confirmation and purchase prediction of the aluminum electrolytic capacitor can be realized. Time can be saved fora user, working efficiency is improved, and platform experience is improved.

Description

technical field [0001] The invention relates to the technical field of decomposition and identification of aluminum electrolytic capacitors, in particular to a method for predicting the purchase of aluminum electrolytic capacitors based on the Mahalanobis distance-based KNN algorithm. Background technique [0002] In the electronic components industry, especially electronic components such as resistors and capacitors, due to the low manufacturing threshold, there are many brands, series and suppliers. At present, for the product determination of aluminum electrolytic capacitors, the original model is generally used to directly locate the product, or the description of the aluminum electrolytic capacitor is directly compared with the symbols in its own database to determine whether it is a product required by the user. Direct positioning through the original model is a more accurate method. However, the method of direct positioning is not suitable for material descriptions. ...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is: for the problem that aluminum electrolytic capacitor is decomposed and recommended difficulty, the present invention provides the parameter decomposition method of aluminum electrolytic capacitor, and the prediction of the KNN (K-NearestNeighbor, K nearest neighbor algorithm, referred to as KNN) method based on Mahalanobis distance The purchase method gives the purchase forecast of the product at the current user, initially screens the list of products that may be purchased for the user, saves time for the user, improves work efficiency, and maximizes the platform experience

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