Design And Implementation Of Visual Real-time Monitoring System For Call Center

As a part of the communication industry,with the rapid development of China’s communications industry,in order to maintain competitiveness,call centers must provide high-quality services.However,as the business volume of diversified large enterprises continues to increase,the service pressure of the call center is also increasing.At the same time,due to the imbalance of the traffic volume,the resource utilization is low.Therefore,a reliable real-time monitoring system is required to monitor various data indicators of the current call center,so that the user can timely understand the current operation status of the call center,and at the same time,It is possible to estimate the future traffic volume of the call center and make reasonable arrangements for the personnel in advance.The system is based on the B / S development architecture,and is fully developed in accordance with the software development process.The development process is divided into four phases: requirements analysis,system design,system implementation,and system testing.The system mainly monitors various data of the call center from three modules,namely the regional monitoring module,the agent monitoring module and the call monitoring module.The system uses the WebSocket message push mechanism to enable the server to push monitoring data to the client in real time,and adds a heartbeat mechanism to WebSocket to enhance the reliability of the system.The front-end part of the system selects the Vue.js framework to enable data layer and view layer Data synchronization,while using ECharts tools to display the data on the page in a graphical way.In addition to monitoring the current data,the system also provides the function of predicting future traffic.By analyzing the characteristics of historical traffic,the LSTM network structure model and exponential smoothing model are used to predict the traffic respectively,and the network model with higher prediction accuracy is finally selected for prediction.The system can monitor and display various data of the call center in real time,enabling users to understand the current operation status of the call center intuitively.In the same time,it can improve the user experience by optimizing the front-end performance.During the traffic prediction process,by analyzing the prediction results of the two models,it is found that the predicted traffic volume using the LSTM network model improves the prediction accuracy by more than 3% and the accuracy rate is about 80%,so the LSTM network structure model is more suitable for Call center traffic forecast.