数据挖掘在智慧城市照明监控系统中的研究与应用

Research and Application of Data Mining in Smart City Lighting Monitoring System

摘要: 我国城市公共照明具有数量多、区域广、用电量巨大，但能源利用率不高等特点。本文将数据挖掘技术引入到智慧城市照明监控系统中，可实现故障预警功能，并对系统运行状态进行评估，优化系统的后续运行。该系统具有智能感知层、网络传输层、数据处理应用层三层架构，可对现有照明设备进行改造，通过在照明设备上安装多种传感器、控制器和通信模块，方便快捷地实现成本低、智能化程度高的智慧城市信息感知网络建设。系统可实时采集城市照明设备的位置及各类运行状态信息，为城市照明提供全方位的设备信息化运维服务。基于数据挖掘技术的故障预警模块解决了传统监控系统报警信息具有滞后性的问题。在故障发生之前产生预警信息，维修人员根据预警信息可及时排除故障隐患，减少因照明设备故障而产生不良的社会影响和经济损失。该系统通过对国内某城市1000条单灯设备数据进行挖掘分析，故障预警成功率为88%，实验数据证明系统具有一定应用及推广价值。

Abstract: China’s urban public lighting has the characters of large number, wide area, huge power consumption, but has not high energy efficiency. In this paper, the data mining technology is introduced into the smart city lighting monitoring system, which can realize the function of fault pre-warning, evaluate the running status of the system, and optimize the subsequent operation of the system. The system has three layers of intelligent perception layer, network transmission layer and data processing application layer. It can transform the existing lighting equipment and realize the low cost conveniently and a variety of sensors, controllers and communication modules can be quickly in-stalled on the lighting equipment. The system can capture the location of city lighting equipment and various operating status information in real time, providing a full range of equipment information operation and maintenance services for city lighting. The fault prewarning module based on data mining technology solves the problem that the alarm information of the traditional monitoring system has a hysteresis. Prewarning information is generated before the fault occurs. Maintenance personnel can timely eliminate the hidden trouble according to the early-warning in-formation and reduce the adverse social impact and economic loss caused by the lighting equipment failure. Mining and analyzing the data of 1000 light equipment in a city, the success rate of fault warning is 88%. The experimental data show that the system has a certain application and promotion value.