基于物联感知的城轨设备智能采集与监控优化OACSTPCD

Aimed at the inefficiency and performance bottleneck of information collection and display of large-scale electromechanical equipment in urban rail transit(URT)ubiquitous in the Internet of things,a method of massive data collection and intelligent processing of perceptual devices is proposed.Through self-identification,self-adaptive and hierarchical connection of data points,batch processing of the equipment status capture was realized,and the intelligent algorithm of graphic and image recognition was used to automatically produce the equipment monitoring interface.According to the logical calculation of real-time data and semantic model transformation,the combined values of multiple groups of data were fitted to reflect the comprehensive state of associated systems and further define the monitoring strategy of classified alarm,to realize data driven intelligent monitoring.The experimental results show that data interface standardization can significantly speed up the access a great quantity of equipment,and the optimized algorithm can make the recognition rate of system drawing to 90％,also,the availability of connecting objects and display reaches more than 85％.The efficiency and accuracy of the monitoring are improved.