Data hierarchical splitting method for composite revenue

Abstract

The invention relates to a composite revenue data hierarchical splitting method, which comprises the following steps: S1, respectively obtaining data sources from a commission return system, a group settlement system, an order system and an input system, the data sources comprising revenue and expenditure data and order data; and S2, synchronizing the data source to a data warehouse center based on hierarchical management, and processing the income and expenditure data and the order data through a splitting model so as to split the income and expenditure data to the corresponding order data, thereby completing wide table construction. Compared with the prior art, the method has the advantages that the hierarchical management data warehouse is adopted, the income and expenditure data from different service systems are split in combination with the order data, the data processing process is simplified. Meanwhile, the order splitting dimension can be increased, and the accuracy of constructing the wide table is guaranteed.

Description

technical field [0001] The invention relates to the technical field of Internet data processing, in particular to a data layering and splitting method for compound revenue. Background technique [0002] In the process of processing revenue data, if a revenue or expenditure cost occurs online or offline, it is usually necessary to split the revenue or expenditure cost into corresponding orders according to a certain logic to calculate different For the benefits of categories or channels, the entire data processing process is relatively complicated, and it needs to rely on the powerful computing power and storage capacity of the data center. [0003] In order to simplify the data processing process, the traditional data processing method is to treat the income or expenditure cost data as an overall expense and include it in the income item or cost item at the end. The proportion of rebates in the contract is relatively large, which leads to the low gross profit rate of some p...

AI Technical Summary

Problems solved by technology

[0002] In the process of processing revenue data, if a revenue or expenditure cost occurs online or offline, it is usually necessary to split the revenue or expenditure cost into corresponding orders according to a certain logic to calculate different The benefits of categories or channels, the entire data processing process is relatively complicated, and it needs to rely on the powerful computing power and storage capacity of the data center

[0003] In order to simplify the data processing process, the traditional data processing method is to treat the income or expenditure cost data as an overall expense and include it in the income item or cost item at the end. The proportion of rebates in the contract is relatively large, which leads to the low gross profit rate of some product order data. If this part of revenue or expenditure cost data is not added in advance, there will be loopholes in evaluating the profitability of the product from the order dimension

[0004] In addition, the current data processing methods usually cannot effectively split the data. On the one hand, due to the excessive data obtained from the business system, preprocessing a large amount of data at the same time will lead to a large amount of redundant data in the data warehouse; on the other hand, If the business rules of the source business system change, the entire data warehouse will also need to be rebuilt, which will affect the entire data processing process and generate a huge amount of calculations. Eventually, the split order dimension cannot meet the requirements, and the constructed wide table data is accurate. Insufficient sex, it can not truly reflect the input and output benefits of the product

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