Eureka AIR delivers breakthrough ideas for toughest innovation challenges, trusted by R&D personnel around the world.

Product reject ratio prediction method and device, computer equipment and storage medium

A forecasting method and defect rate technology, applied in the computer field, can solve problems such as low accuracy of default rate and low accuracy of defect rate prediction, and achieve the effects of improved accuracy, comprehensive prediction, and accurate calculation

Pending Publication Date: 2019-08-09
平安直通咨询有限公司
View PDF0 Cites 1 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] Traditionally, ewing generally calculates the default rate first, and then predicts the overall NPL based on the aging distribution or rolling rate, resulting in a low accuracy of the predicted default rate, which in turn makes the prediction of the NPL rate inaccurate.

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Product reject ratio prediction method and device, computer equipment and storage medium
  • Product reject ratio prediction method and device, computer equipment and storage medium
  • Product reject ratio prediction method and device, computer equipment and storage medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0066] In order to make the purpose, technical solutions, and advantages of this application clearer, the following further describes the application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the application, and not used to limit the application.

[0067] The product defect rate prediction method provided in this application can be applied to figure 1 In the application environment shown. Wherein, the terminal 102 and the server 104 communicate through the network. Specifically, when the terminal 102 receives the product failure rate prediction instruction, it can start to predict the product failure rate. For example, it can first obtain the corresponding product data from the server 104 according to the product identifier, and calculate the predicted default rate based on the product data. Then receive the initial loan balance growth, initial write-off pl...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention relates to the field of big data processing, in particular to a product reject ratio prediction method and device, computer equipment and a storage medium. The method comprises the following steps: receiving an input product reject ratio prediction instruction, wherein the product reject ratio prediction instruction carries a product identifier; obtaining product data corresponding to the product identifier, and calculating according to the product data to obtain a predicted default rate; receiving an input initial loan balance growth condition, an initial verification and salesplan and an initial recovery rate; obtaining a predicted product loan balance according to the initial loan balance growth condition; calculating a product loss balance according to the predicted default rate, the initial recovery rate and the verification and sales plan; and calculating the ratio of the product loss balance to the product loan balance to obtain the reject ratio. By adopting the method, the reject ratio prediction accuracy of products can be improved.

Description

Technical field [0001] This application relates to the field of computer technology, in particular to a method, device, computer equipment, and storage medium for predicting product failure rate. Background technique [0002] For the prediction of non-performing rate risk, etc., it is necessary to draw a default rate curve based on historical data, then predict the future default rate based on the direct regression method, and finally calculate the non-performing rate based on the default rate. [0003] Traditionally, ewing first calculates the default rate, and then predicts the overall defect based on the age distribution or rolling rate, which leads to the low accuracy of the predicted default rate, which in turn makes the prediction accuracy of the non-performing rate low. Summary of the invention [0004] Based on this, it is necessary to provide a method, device, computer equipment, and storage medium for predicting product failure rate that can improve the accuracy of product...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q10/06G06Q40/02
CPCG06Q10/04G06Q10/06395G06Q40/03
Inventor 莫泽鸿范荣
Owner 平安直通咨询有限公司
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Eureka Blog
Learn More
PatSnap group products