智能电能表光通信模块的研究,在智能电网建设中发挥着重要作用。通过对EPON(以太网无源光网络)、PLC(电力载波通信)和WiMAX(全球微波互联接入)技术的对比分析,选择EPON作为智能电能表通信接入网技术,并设计了通信流程。基于QCA8829嵌入...智能电能表光通信模块的研究,在智能电网建设中发挥着重要作用。通过对EPON(以太网无源光网络)、PLC(电力载波通信)和WiMAX(全球微波互联接入)技术的对比分析,选择EPON作为智能电能表通信接入网技术,并设计了通信流程。基于QCA8829嵌入式芯片,在Redhat Linux 2.6.x开发平台上采用可接入EPON系统的光纤接口技术,实现了智能电能表主站与从站的通信系统。经测试表明,基于EPON智能电表光通信模块实现了智能电网配电侧信息全采集、全覆盖,并使远程电费控制及负载控制到户。展开更多
Data warehouse (DW), a new technology invented in 1990s, is more useful for integrating and analyzing massive data than traditional database. Its application in geology field can be divided into 3 phrases: 1992-1996,...Data warehouse (DW), a new technology invented in 1990s, is more useful for integrating and analyzing massive data than traditional database. Its application in geology field can be divided into 3 phrases: 1992-1996, commercial data warehouse (CDW) appeared; 1996-1999, geological data warehouse (GDW) appeared and the geologists or geographers realized the importance of DW and began the studies on it, but the practical DW still followed the framework of DB; 2000 to present, geological data warehouse grows, and the theory of geo-spatial data warehouse (GSDW) has been developed but the research in geological area is still deficient except that in geography. Although some developments of GDW have been made, its core still follows the CDW-organizing data by time and brings about 3 problems: difficult to integrate the geological data, for the data feature more space than time; hard to store the massive data in different levels due to the same reason; hardly support the spatial analysis if the data are organized by time as CDW does. So the GDW should be redesigned by organizing data by scale in order to store mass data in different levels and synthesize the data in different granularities, and choosing space control points to replace the former time control points so as to integrate different types of data by the method of storing one type data as one layer and then to superpose the layers. In addition, data cube, a wide used technology in CDW, will be no use in GDW, for the causality among the geological data is not so obvious as commercial data, as the data are the mixed result of many complex rules, and their analysis always needs the special geological methods and software; on the other hand, data cube for mass and complex geo-data will devour too much store space to be practical. On this point, the main purpose of GDW may be fit for data integration unlike CDW for data analysis.展开更多
文摘智能电能表光通信模块的研究,在智能电网建设中发挥着重要作用。通过对EPON(以太网无源光网络)、PLC(电力载波通信)和WiMAX(全球微波互联接入)技术的对比分析,选择EPON作为智能电能表通信接入网技术,并设计了通信流程。基于QCA8829嵌入式芯片,在Redhat Linux 2.6.x开发平台上采用可接入EPON系统的光纤接口技术,实现了智能电能表主站与从站的通信系统。经测试表明,基于EPON智能电表光通信模块实现了智能电网配电侧信息全采集、全覆盖,并使远程电费控制及负载控制到户。
文摘Data warehouse (DW), a new technology invented in 1990s, is more useful for integrating and analyzing massive data than traditional database. Its application in geology field can be divided into 3 phrases: 1992-1996, commercial data warehouse (CDW) appeared; 1996-1999, geological data warehouse (GDW) appeared and the geologists or geographers realized the importance of DW and began the studies on it, but the practical DW still followed the framework of DB; 2000 to present, geological data warehouse grows, and the theory of geo-spatial data warehouse (GSDW) has been developed but the research in geological area is still deficient except that in geography. Although some developments of GDW have been made, its core still follows the CDW-organizing data by time and brings about 3 problems: difficult to integrate the geological data, for the data feature more space than time; hard to store the massive data in different levels due to the same reason; hardly support the spatial analysis if the data are organized by time as CDW does. So the GDW should be redesigned by organizing data by scale in order to store mass data in different levels and synthesize the data in different granularities, and choosing space control points to replace the former time control points so as to integrate different types of data by the method of storing one type data as one layer and then to superpose the layers. In addition, data cube, a wide used technology in CDW, will be no use in GDW, for the causality among the geological data is not so obvious as commercial data, as the data are the mixed result of many complex rules, and their analysis always needs the special geological methods and software; on the other hand, data cube for mass and complex geo-data will devour too much store space to be practical. On this point, the main purpose of GDW may be fit for data integration unlike CDW for data analysis.
