Design And Implementation Of Intelligent Customer Service System For The Operation And Maintenance Field

In recent years,machine learning methods have driven the development of many fields.Neural network-based landing applications are serving our daily lives.Intelligent customer service is a typical representative among them.With the support of automated customer service systems,machines have been formed.The new model combined with manual customer service is applied in various fields to serve different target groups.Therefore,this article will focus on the current research hotspots and combine the actual needs of Huarong Rongtong(Beijing)Technology Co.,Ltd.to develop an intelligent customer service system for the company's operation and maintenance field to serve the company's internal employees.The system has designed and realized the four main functions of intelligent matching,customer service switching,system management,and corpus management,and added machine learning methods to make the customer service system more intelligent,thereby improving the work efficiency of employees and assisting the company's internal construction.In response to the appeal,this article will focus on the following three aspects:(1)Realize the four functions of intelligent customer service and connect with various internal systems of the companyThis research has designed four main functions to be applied to the intelligent customer service system in a targeted manner,namely:intelligent matching,customer service switching,corpus management and system management.As the back end of the service,different connection methods are rationally designed to connect with other internal product systems of the company,and different knowledge bases are designed and implemented according to the requirements of various departments to provide personalized services.(2)Design and implement an intelligent customer service text matching modelQuestion matching is one of the important functions of this intelligent customer service system.It matches the most relevant questions in the corpus for employees in response to the input problems of the company's employees.Because traditional keyword matching can't understand the user's true intentions well,this system uses machine learning algorithms to design and implement two text matching models,namely:a text matching model based on Word2Vec and TF-IDF,and a text matching model based on self-attention and BiLSTM's text matching model.Compared with traditional text matching,the model greatly improves the accuracy of matching,changes the drawbacks of the previous machine's misinterpretation of intentions,and makes the system more intelligent and humane.(3)Modularized and packaged design of the systemIn response to the pain points found in practical applications,the system framework was modularized and improved to form three parts:network back-end,asynchronous communication back-end,and message processing back-end.The work of each module was more detailed and distributed.Compared with the traditional single-process structure,the asynchronous architecture improves the concurrency of the system.At the same time,the system uses various methods such as pip and docker for packaging design,avoiding the need to configure the system environment during system migration,and improving the operability of the system.At the same time,after packaging,you can use the command line to configure the system parameters to avoid directly modifying the source code,and the system security is guaranteed.