Commodity inventory risk early warning method and system based on statistical quartile distance and computer readable storage medium

Abstract

The invention discloses a commodity inventory risk early warning method based on statistics of quartile distances. The method comprises the steps of obtaining original commodity inventory data of allstores in a certain historical time period; calculating according to the original commodity inventory data to obtain inventory incremental data; calculating an upper quartile and a lower quartile of the inventory incremental data, and calculating a quartile distance and an anomaly detection threshold according to the upper quartile and the lower quartile; and detecting whether the new inventory increment exceeds an abnormality detection threshold, and if so, judging the new inventory increment as abnormal data and pushing the abnormal data to a front-end early warning. The invention also provides a commodity inventory risk early warning system based on the statistical quartile distance, and a computer readable storage medium containing the method. According to the invention, the problems of large calculation overhead, abnormal value missed judgment when the data volume is large, low timeliness and the like in the prior art are solved.

Description

technical field

[0001] The invention belongs to the application of big data in the field of retail risk control, and in particular relates to a method, system, and computer-readable storage medium for commodity inventory risk warning based on statistical interquartile ranges. Background technique

[0002] At present, the physical stores of large retail enterprises are growing at an extremely fast rate. At the same time, due to the limitations of management, store managers may conduct illegal operations for their own self-interest and cause major economic losses to the enterprises. At present, they often rely more on post-event financial audits, Inventory and other methods to check the problem, the timeliness is very low, and it is often difficult to fully recover the financial loss after the problem is found, so we need to find an efficient and accurate method to detect suspicious in time through the monitoring and analysis of sales and financial indicators risk and notify r...

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