Cross-platform multi-party data efficient blending method and device

Abstract

The invention belongs to the technical field of data blending, and discloses a cross-platform multi-party data efficient blending method and device, when a blending source is two parties, two-party blending is directly carried out on source data of the two parties, and corresponding blending files and error files are output through blending; and when the blending source is larger than the two parties, the process repeatedly carries out source data blending of the two parties, blending outputs a corresponding blending file and an error file, the blending file serves as a source file of one party of two-party blending in the next round, balance blending is carried out until the final blending source is equal to the two parties, and blending outputs a final blending file. According to the invention, cross-platform, transplantable and efficient blending is realized.

Description

technical field [0001] The invention belongs to the technical field of data blending, and in particular relates to a cross-platform multi-party data blending method and device with high efficiency. Background technique [0002] As the country vigorously promotes inclusive finance, mobile payment has also been widely developed and popularized rapidly, quickly reaching all walks of life, and forming a variety of digital payment systems. However, the system relationship between upstream and downstream has also become more and more complex, from a single upstream and downstream relationship to a multi-node complex upstream and downstream relationship. Each payment system has multiple downstream channel merchants and multiple upstream payment channel systems , Moreover, the average daily transaction volume of the payment system has increased exponentially every year. Therefore, on D+1 day, how can the system quickly collate transaction details with multiple upstream payment chan...

AI Technical Summary

Problems solved by technology

[0004] 1) Due to the wide variety of upstream system architectures and various data sources for blending (messages, files, and storage devices, etc.), the system does not form a unified format for blending, and the blending mechanism is also different from system to system;

[0005] 2) In the blending process of the traditional system, there are many associated components (such as: storage devices, message middleware, and cache components, etc.), and there are many interactions between components in the blending project, the processing is very time-consuming, and the performance is not good. Completely improved, as the amount of data increases, the performance of blending becomes lower and lower;

[0006] 3) The blending process lacks integrity and continuity. After the blending is completed, no flattening documents can be produced for downstream use, but need to be processed through a separate mechanism;

[0007] 4) The traditional blending mechanism is generally two-party blending, but the actual business scenario is mostly multi-party blending, and the balance difference of multi-party blending needs to be discovered manually, which increases labor costs;

[0008] 5), the traditional blending mechanism generally needs to rely on the development language and platform, and cannot form a universal, efficient, cross-platform, and portable blending mechanism;

[0009] To sum up, the structure of traditional payment systems varies greatly, the system complexity is high, and there are many middlewares that rely on. Therefore, the development cycle of realizing the blending logic takes a long time, and it is impossible to form a uniform blending mechanism, and the blending process lacks integrity. stability and continuity, the balance check increases labor costs, and there are many related components in the system, the performance cannot be fully utilized, and a cross-platform, portable, and efficient blending method cannot be realized

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