Monitoring method and monitoring system for real-time anti-cheating detection, and computing equipment

Abstract

The invention relates to a monitoring method for real-time anti-cheating testing. The method comprises the steps of receiving a real-time log file, and obtaining time sequence data and real-time characteristic data after preprocessing; performing pre-estimation on the time sequence data, and detecting a time sequence abnormity; and performing distribution abnormity detection on the real-time characteristic data, and generating a monitoring strategy according to the time sequence abnormity detection result. According to the monitoring method, the monitoring system and the computing equipment, coordination of two detections of time sequence pre-estimation and characteristic distribution abnormity can be realized, thereby realizing two effects of real-time abnormity recalling and strategy monitoring early-warning.

Description

technical field [0001] The present disclosure relates to the technical field of the Internet, and in particular to a real-time anti-cheating detection and monitoring method, system and computing equipment. Background technique [0002] With the development of Internet technology, O2O (Online to Offline, online to offline) has emerged as a business model accepted by consumers. The basic business logic of O2O is that users pay in advance on online platforms, and then experience offline consumption, and merchants track their marketing effects in real time, thus forming a closed-loop business service and experience process. Due to the effect of brand effect and agglomeration effect, more and more merchants choose to rely on established Internet platforms to promote and provide services such as goods and labor services, and correspondingly, O2O businesses that place advertisements through Internet platforms have also been derived. . [0003] In response to the increasingly ramp...

AI Technical Summary

Problems solved by technology

[0015] 1. Rules and offline mining cannot make targeted discovery and processing of abnormalities that merchants can actually perceive, such as short-term consumption surges, so that it is impossible to actively and completely eliminate such abnormalities;

[0016] 2. Case collection and offline mining are limited by the delay of feedback and log time, so it is impossible to quickly and timely discover new cheating locations and patterns, nor can it give early warning of upcoming large-scale cheating;

[0017] 3. Too much reliance on manual processing in the monitoring system requires manual analysis, judgment and operation for newly discovered abnormalities; and

[0018] 4. It is impossible to directly give the scope and quantity of cheating, and it is impossible to estimate the data under normal conditions when there is no cheating

Images