Service quality evaluation system and method based on sub-time window deep reinforcement learning

Abstract

The invention provides a service quality evaluation system based on sub-time window deep reinforcement learning. The system comprises a data acquisition module, a model adjustment module, a reward feedback module, a parallel learning module, a Q table updating module, a periodic iteration module, a prediction learning module and a time window adjustment module. According to the method, the problems of low evaluation accuracy, poor timeliness and the like in an existing quality evaluation method are solved by utilizing the interactivity and decision-making ability of reinforcement learning andthe perception ability of deep learning.

Description

technical field [0001] The invention relates to a service quality evaluation system, in particular to a service quality evaluation system and an evaluation method. Background technique [0002] With the continuous development of my country's economic level, the service industry has also been prosperous. The scale of the service industry is huge, whether it is the traditional real economy, such as supermarkets, hospitals, etc., or the online Internet economy, such as e-commerce (Taobao, JD.com, etc.), digital media operators (Weibo, Douyin, etc.), etc. , its service ecology relies on a large number of service providers, such as supermarkets, medical networks, Taobao merchants, video producers, and program producers. For such a large number of service providers, how should operators evaluate their service quality, so that the evaluation results can reflect the real situation, so that they can be distinguished according to the service quality of service providers. On the one h...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to provide a service quality evaluation system based on time-divided deep reinforcement learning, which uses the interactivity and decision-making ability of reinforcement learning and the perception ability of deep learning to solve the problem of low evaluation accuracy in existing quality evaluation methods , poor timeliness and other issues

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