针对我国碳排放工业类型多、碳排放监测数据源多样的问题,设计了一个基于多源异构数据的能源电力碳排放监测诊断服务系统.系统由非分光红外探测技术、改进型微分粒子群算法(particle swarm optimization,PSO)、云计算、对象链接与嵌入统一架构(OLE for process control-unified architecture,OPC-UA)技术等构成.通过改进PSO算法来提高收敛速度,进一步提高数据监测和处理效率.采取OPC-UA技术实现对碳排放多源异构数据进行统一传输和反馈,极大地缓解了系统主机的计算压力.试验结果表明,经系统技术核算的数据误差率在可接受范围内,为其他技术研究奠定基础.
Research on Energy and Electricity Carbon Emission Monitoring and Diagnosis Service System Based on Multisource Heterogeneous Data
In response to the problem of multiple types of carbon emission industries and diverse sources of carbon emission monitoring data in China,a carbon emission monitoring and diagnosis service system for energy and electricity based on multi-source heterogeneous data was designed.The system was composed of non-spectral infrared detection technology,improved differential particle swarm optimization(PSO),cloud computing,object linking and embedding unified architecture Technology OLE for process control-unified architecture(OPC-UA),and other technologies.By improving the PSO algorithm to improve the rate of convergence,the efficiency of data monitoring and processing can be further improved.Adopting OPC-UA technology to achieve unified transmission and feedback of carbon emission multi-source heterogeneous data can greatly alleviate the computational pressure on the system host.The experimental results indicate that the data error rate calculated by the system technology is within an acceptable range,laying the foundation for other technical researches.
NIRD technologyparticle swarm optimization(PSO)algorithmOLE for process control-unified architecture(OPC-UA)technologycarbon emission monitoring
万方数据
维普
