A compatible carbon efficiency information service framework based on the industrial internet identification

Green and low-carbon is a new development model that seeks balance between environmental sustainability and high economic growth.If explainable and available carbon emission data can be accurately obtained,it will help policy regulators and enterprise managers to more accurately implement this development strategy.A lot of research has been carried out,but it is still a difficult problem that how to accommodate and adapt the complex carbon emission data computing models and factor libraries developed by different regions,different industries and different enterprises.Meanwhile,with the rapid development of the Industrial Internet,it has not only been used for the supply chain optimization and intelligent scheduling of the manufacturing industry,but also been used by more and more industries as an important way of digital transformation.Especially in China,the Industrial Internet identification and resolution system is becoming an important digital infrastructure to uniquely identify objects and share data.Hence,a compatible carbon efficiency information service framework based on the Industrial Internet Identification is proposed in this paper to address the problem of computing and querying multi-source heterogeneous carbon emission data.We have defined a multi cooperation carbon emission data interaction model consisting of three roles and three basic operations.Further,the implementation of the framework includes carbon emission data identification,modeling,calculation,query and sharing.The practice results show that its capability and effectiveness in improving the responsiveness,accuracy,and credibility of compatible carbon efficiency data query and sharing services.

A unified multi-view multi-person tracking framework

Despite significant developments in 3D multi-view multi-person (3D MM) tracking, current frameworks separately target footprint tracking, or pose tracking. Frameworks designed for the former cannot be used for the latter, because they directly obtain 3D positions on the ground plane via a homography projection, which is inapplicable to 3D poses above the ground. In contrast, frameworks designed for pose tracking generally isolate multi-view and multi-frame associations and may not be sufficiently robust for footprint tracking, which utilizes fewer key points than pose tracking, weakening multi-view association cues in a single frame. This study presents a unified multi-view multi-person tracking framework to bridge the gap between footprint tracking and pose tracking. Without additional modifications, the framework can adopt monocular 2D bounding boxes and 2D poses as its input to produce robust 3D trajectories for multiple persons. Importantly, multi-frame and multi-view information are jointly employed to improve association and triangulation. Our framework is shown to provide state-of-the-art performance on the Campus and Shelf datasets for 3D pose tracking, with comparable results on the WILDTRACK and MMPTRACK datasets for 3D footprint tracking.