摘要
为避免工单调度过程中标签数据相互干扰,设计基于信息熵的工单标签大数据并行分类系统。整合大数据采集器、并行处理器元件内传输的业务工单标签信息参量,设定信息熵查询序列条件,通过变分模态分解业务工单调度数据信息,得到并行参数优化处理结果,实现软件执行环境搭建。结合相关硬件设备结构,完成工单标签大数据并行分类系统设计。系统性能测试结果表明,设计系统的信息熵序列能够有效降低工单标签信息参量之间的干扰强度,符合按需分类业务工单标签信息参量的实际应用需求。
In order to avoid the interference of tag data in the process of work order scheduling,a parallel classification system of work or-der tag big data based on information entropy is designed.It integrates the service work order label information parameters trans-mitted in the big data collector and parallel processor elements,sets the information entropy query sequence conditions,decom-poses the service work order scheduling data information through the variational mode,obtains the parallel parameter optimiza-tion processing results,and realizes the construction of the software execution environment.Combined with the structure of rele-vant hardware equipment,the design of the parallel classification system of work order label and big data is completed.The sys-tem performance test results show that the information entropy sequence of the designed system can effectively reduce the inter-ference intensity between the work order label information parameters,and meet the practical application needs of the work order label information parameters of the classified service on demand.
作者
张云飞
赵婉茹
段玉玮
王婧骅
高姗
ZHANG Yun-fei;ZHAO Wan-ru;DUAN Yu-wei;WANG Jing-hua;GAO Shan(State Grid Shanghai Electric Power Company,Shanghai 200030 China)
出处
《自动化技术与应用》
2024年第10期139-143,共5页
Techniques of Automation and Applications
基金
上海市电力公司科技项目(B3090D210000)。
关键词
信息熵序列
业务工单调度
大数据
分类
变分模态分解
information entropy sequence
business work order scheduling
big data
classification
variational modal decomposition
