This paper proposed an integrated architecture and case study highlighting practices for developing cloud computing system in smart manufacture. It developed an electronic purchase order web-based system with four business units. The research method is based on applying Rational Unified Process and ICAM DEFinition Model (IDEF). It uses scenario and IDEF model to represent the user requirements, and object oriented technology to construct smart system. It also drives critical Key Performance Indexes (KPI) to evaluate the benefits of enterprises adopting the proposed architecture. The result shows that this architecture and methodology can reduce process time, reduce processing cost, improve accuracy, efficiency.

Enterprises are increasingly using big data analytics and intelligence to solve business processes between original equipment manufacturers (OEMs) and original design manufacturers (ODMs) to increase the competitiveness and improve the efficiency of solutions. This research applies a cloud computing-based enterprise resource planning (ERP) system and OEM/ODM system to business to business (B2B) e-commerce. The integration of smart ERP system with B2B e-commerce can achieve the expected benefits of both. It reduces cost and improves the work efficiency, thereby improving the competitiveness of enterprises. In comparison to other traditional methods, the proposed system can make business management more efficient, promote the optimisation of cost structure, enhance the accuracy of sales orders, and improve the response time. It can also make the use of resources more reasonable.

Different from the conventional point source localization, rigid body localization (RBL) not only aims to estimate the position of the target but also to acquire the attitude information, which is also essential information in many Internet of Things (IoT) applications, such as the virtual reality systems, smart parking systems. This paper develops three maximum likelihood estimators (MLEs) for the RBL purpose in 3 dimensional space via a single base station. The MLEs are designed for the RBL framework, which adopts the direction of arrival (DoA) of the signal from a small scale wireless sensor network (SSWSN) mounted on the surface of the rigid target as measurement and can be realized by a single base station. The three MLEs respectively exploit the SSWSN topology information, the DoA measurement information only, as well as the equality constraint of the rotation matrix and the DoA measurement information. In addition, we implement the modified Guass-newton algorithm for the MLEs of the rotation matrix and the translation vector. Simulations show that the proposed MLE fusing the equality constraint of the rotation matrix and the DoA measurement information most approaches the Cramer-Rao Lower Bound and also outperforms the other two MLEs in terms of convergence success rate and the computational cost.

In the current environment where the network and the real society are intertwined, the network public view of public emergencies has involved in reality and altered the ecology of communal public views in China. A new online court of influence has been created, and it affected the trend of events. As the main type of public emergencies, public health emergencies are directly related to people’s health and life insurance. Therefore, the public often pays special attention. At present, correct media guidance plays an irreplaceable and important role in calming people’s hearts and stabilizing social order. If news and public view are left unchecked, it is likely to cause panic among the people. However, in reality, public view research has always been a research object that is difficult to intelligentize and quantify. Based on such a realistic background, the article conducts a research on public view of public health emergencies based on artificial intelligence data analysis. This study designs an expert system for network public view and optimizes the algorithm for the key problem: SFC deployment. Finally, the system was put into real news and public opinion research on new coronavirus epidemic prevention, and experimental tests were carried out. The experimental results have shown that in the new coronavirus incident, the nuclear leakage incident, and the epidemic prevention policy, the data obtained by the public through the Internet are 50%, 68.06%, and 64.35%, respectively. For the system function in this study, both ICSO and IPSO are far better than the optimization results of CSO and PSO. For most of the test functions, IPSO is better than ICSO’s optimization results, which better fulfills the needs of the research content. This study will make an in‐depth analysis of the evolution process of online public opinion on public emergencies from the macro‐, meso‐, and micro‐perspectives, in order to analyze the dissemination methods and internal evolution mechanism of various public emergencies of online public opinion, which provides countermeasures and suggestions for the government to guide and manage network public opinion.