A supply chain online transaction management method and system

CN122243504APending Publication Date: 2026-06-19FOSHAN HUISHE SPACE INFORMATION TECHNOLOGY CO LTD

Patent Information

Authority / Receiving Office: CN · China
Patent Type: Applications(China)
Current Assignee / Owner: FOSHAN HUISHE SPACE INFORMATION TECHNOLOGY CO LTD
Filing Date: 2026-03-18
Publication Date: 2026-06-19

AI Technical Summary

Technical Problem

In the process of online supply chain transaction management, inconsistent data source formats and transmission protocols lead to large data integration errors, making it impossible to guarantee the accuracy of transaction data.

Method used

By collecting static basic data and dynamic transaction behavior data from supply chain transaction participants, standardizing the data, and generating standardized static and dynamic datasets, credit scoring models are used for credit rating matching. Combined with behavioral predictive analysis and multi-dimensional parameter combinations, the optimal matching algorithm type is identified, transactions are executed, and performance evaluation and feedback updates are performed.

Benefits of technology

It achieves consistency detection of data sources, reduces data integration errors, improves the accuracy of credit rating matching and the precision of transaction matching, and ensures the accuracy and real-time adaptability of transaction data.

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Abstract

This invention relates to the field of supply chain technology and discloses a method and system for online supply chain transaction management. The system includes a data acquisition and processing module, a transaction matching and analysis module, and a transaction execution and optimization module. During real-time acquisition of supply chain transaction data, by establishing data standardization parameters and setting unified format standards for different data sources, the system ensures consistency between static basic data and dynamic transaction behavior data. Simultaneously, the data is imported into a standardized processing unit for real-time format unification and outlier removal. This allows for real-time detection of format and transmission protocol deviations in the data source, ensuring the accuracy of the transaction data and reducing data integration errors. Furthermore, by calculating credit scores using a pre-trained credit scoring model, the system can determine the success of credit rating matching in real time, thereby reducing the likelihood of deviations in credit rating matching.

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