Method for analyzing networked transaction data collection and transaction completion degree

Abstract

The invention provides a method for analyzing the networked transaction data collection and transaction completion degree. Collected networked transaction evaluation data are calculated based on the reverse credit cloud algorithm to obtain numerical characteristics reflecting various credit evaluation index qualitative concepts; the virtual cloud comprehensive algorithm is adopted to obtain the comprehensive credit cloud of a main body; the numerical characteristics of the comprehensive credit cloud are calculated based on the forward direction credit cloud algorithm, the credit evaluation situation is restored, a series of credit cloud droplet data is generated, a credit evaluation cloud picture can be drawn, and the credit is an important measurement index of the networked transaction completion degree. By means of the method, randomness and fuzziness produced in the networked transaction credit evaluation process are well solved, an evaluation model put forward based on the credit cloud can reflect the evaluation index from multiple angles and multiple levels, and comprehensiveness, objectivity and authenticity of the evaluation result can be guaranteed. The transaction completion degree analyzing is carried out according to the evaluation result, and a reliable reference basis can be provided for the networked transaction platform operation and the network resource allocation.

Description

technical field [0001] The invention is a network transaction data collection and transaction completion degree analysis method, which is mainly used to solve the problems of credit uncertainty measurement expression, credit evaluation of participating subjects and transaction completion degree analysis in the network transaction environment, and belongs to the field of service trust and reputation management . Background technique [0002] Since entering the 21st century, with the rapid development of network technology and the increasingly perfect network infrastructure, the Internet has been popularized rapidly, and network transactions based on the Internet have also achieved unprecedented development. Due to its unique advantages, online transactions have developed explosively, and its future will have even greater potential for value creation and room for development. Compared with the traditional way of buying and selling, Internet-based network transactions can carr...

AI Technical Summary

Problems solved by technology

The main reasons are as follows: First, the results of the two methods of probability model and fuzzy set theory are finally reduced to precise values, so that the trust information no longer has the slightest ambiguity; second, neither the probability model nor the fuzzy set theory method takes credit The essential attribute of the fusion of randomness and fuzziness, the credit management method based on the probability model considers the randomness of the credit and ignores the fuzziness, while the credit management method based on fuzzy mathematics pays attention to the fuzziness and ignores the randomness; the third is fuzziness In the evaluation process of the set theory evaluation method, it is difficult to obtain the fuzzy evaluation matrix, which is highly subjective. The method of determining the membership function is not very strict and scientific; the fourth is that the rough set method can reduce the amount of data, but after attribute reduction The information will be relatively incomplete

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