随着云计算技术和移动互联网技术的快速发展，呼叫中心正在逐步向云联络中心过渡。相比于传统的呼叫中心，云联络中心在接入类型上和接入渠道上更加丰富，囊括了音频、视频以及消息通信，同时接入了微信、微博、APP等多个移动互联网平台。这也给云联络中心系统的设计和实现带来了两个新的挑战：1、随着媒体类型和接入渠道的不断扩增，系统中业务数据的种类不断增多、计算更新节奏不断加快。这大大增加了系统业务数据的复杂性，并对业务数据的实时处理提出了更高的要求；2、随着系统业务模块不断增多、模块分布的节点范围不断增大，系统需要对其自身的运行状况提供一种更有效的实时监控方式，帮助开发、测试及维护人员进行故障定位和运行指标的统计分析。

      针对云联络中心业务数据的维护，本文设计和实现了状态服务器。状态服务器是云联络中心的一个业务子系统，它负责维护系统中与坐席有关的实时业务数据。本文从功能模块的角度详细介绍了状态服务器的设计与实现过程：首先介绍了通信模块，给出了完整的消息接入流程和通信格式定义；其次介绍了统计指标计算模块，该模块对每一类统计指标设计和实现了对应的计算类，并通过实现坐席实时状态和统计指标的可配置型大大增强了坐席业务数据在维护上的灵活性；最后介绍了路由请求模块，详细介绍了该模块的路由请求处理流程以及路由策略的设计与实现过程。

      针对云联络中心运行状况的实时监控，本文以系统日志为切入点，设计和实现了一套针对云联络中心业务特点的日志分析系统。日志分析系统以开源的日志分析平台Elastic栈工具和流平台Kafka为基础，包含了日志收集、日志实时流处理和日志查询三个模块。对于日志收集模块，本文详细介绍了其配置信息和采集流程，并给出了在日志采集流程中日志格式的变化示例。为了帮助相关人员更直观地监控系统运行状况和更快速有效地进行故障定位，本文在介绍日志实时流处理模块时提出了基于会话事件序列图和会话序列图的系统监控与故障定位方式，并详细阐述了它的设计和实现流程。最后在介绍日志查询模块时，本文结合示例详细介绍了日志查询的接口，并展示了Kibana平台的日志聚合图表。

       本文在介绍完每一个系统的详细设计与实现后对系统进行了功能测试和性能测试，保证系统符合预期的设计与使用需求。

With the rapid development of cloud computing technology and mobile Internet technology, the call center is gradually transitioning to the cloud contact center. The cloud contact center is more abundant in its access type and access channel than the traditional call center, which include audio, video and messaging communication and can also access to WeChat, Weibo, APP and the other mobile internet platforms. Because of this the cloud contact is faced with two new challenges in its design and implementation: 1. As the media type and access channels continue to expand, the types of the system business data is increasing and the calculation pace of it is accelerating, which greatly increases the complexity of the business data and presents a higher demand for processing the data in terms of real-time; 2. As the system business module continues to grow and the range of nodes distributed by the module continues to increases, the system needs to be real-time monitored in a more effective way, which can help the developers and the testers of the system to debug system, locate system faults and analyze running statistics.

In order to maintain the business data of cloud contact center, this paper designs and implements XStateServer. XStateServer is a business system of the cloud contact center, which is responsible for maintaining real-time business data related to agents in the system. The design and implementation of the XStateServer are introduced in detail from the perspective of functional modules in the paper. Firstly, the communication module is introduced, which includes the complete message access flow and the format definition of messages. Secondly, the statistics calculation module is introduced. This module designs and implements a calculator class for each type of the statistics and greatly enhances the flexibility of the agent business data in the maintenance through the configuration of the real-time status indicator and statistics indicators. Finally, the route request module is introduced, which includes the complete routing request processing flow and the design and implementation of routing strategies.

In order to monitor the cloud contact center in real-time, this paper designs and implements a log analysis system called LogAnalyzer based on the system log. LogAnalyzer is based on the open source log analysis platform Elastic Stack and the streaming platform Kafka and it includes three functional modules: log collection module, log stream processing module and log query module. For the log collection module, this paper introduces its configuration information and collection flow in detail, and gives an example of the format change of logs during collection process. In order to help the relevant personnel to monitor the running status of the system more intuitively and to locate the fault more quickly and effectively, this paper presents a new way of system monitoring and fault location based on the session-event sequence diagram and the session sequence diagram and elaborates the design and implementation process of it in the introduction of the log steaming process module. Finally, the log query module is introduced. This paper introduces the log query APIs in detail with examples, and shows log aggregation graphs of the Kibana platform.

After the introduction of the detailed design and implementation of each system, this paper performs functional tests and performance tests on the system, which ensures that the system meets the expected design and use requirements