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It will use SolidWorks to design structure of Manipulator and to establish the Three-dimensional Modeling based on automated welding robot of Shanghai Bamac Electric Technology Co., Ltd.,then by using the plug of simulation and motion of SolidWorks, it will focus on the Motion Simulation and Finite Element Analysis of the Manipulator. Finally the trajectory and the work space of the Manipulate can be received, and provide a basis of manipulator analysis in order to optimize the manipulator for the company.

A growing number of web users around the world have started to post their opinions on social media platforms and offer them for share. Building a highly scalable evolution prediction model by means of evolution trend volatility plays a significant role in the operations of enterprise marketing, public opinion supervision, personalized recommendation, and so forth. However, the historical patterns cannot cover the systematical time-series dynamic and volatility features in the prediction problems of a social network. This paper aims to investigate the popularity prediction problem from a time-series perspective utilizing dynamic linear models. First, the stationary and nonstationary time series of Weibo hot events are detected and transformed into time-dependent variables. Second, a systematic general popularity prediction model N- SEP 2 M is proposed to recognize and predict the nonstationary event propagation of a hot event on the Weibo social network. Third, the explanatory compensation variable social intensity (SI) is introduced to optimize the model N- SEP 2 M. Experiments on three Weibo hot events with different subject classifications show that our prediction approach is effective for the propagation of hot events with burst traffic.

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In recent years, COVID-19 has become the hottest topic. Various issues, such as epidemic transmission routes and preventive measures, have “occupied” several online social media platforms. Many rumors about COVID-19 have also arisen, causing public anxiety and seriously affecting normal social order. Identifying a rumor at its very inception is crucial to reducing the potential harm of its evolution to society as a whole. However, epidemic rumors provide limited signal features in the early stage. In order to identify rumors with data sparsity, we propose a few-shot learning rumor detection model based on capsule networks (CNFRD), utilizing the metric learning framework and the capsule network to detect the rumors posted during unexpected epidemic events. Specifically, we constructively use the capsule network neural layer to summarize the historical rumor data and obtain the generalized class representation based on the historical rumor data samples. Besides, we calculate the distance between the epidemic rumor sample and the historical rumor class-wise representation according to the metric module. Finally, epidemic rumors are discriminated against according to the nearest neighbor principle. The experimental results prove that the proposed method can achieve higher accuracy with fewer epidemic rumor samples. This approach provided 88.92% accuracy on the Chinese rumor dataset and 87.07% accuracy on the English rumor dataset, which improved by 7% to 23% over existing approaches. Therefore, the CNFRD model can identify epidemic rumors in COVID-19 as early as possible and effectively improve the performance of rumor detection.