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The novel coronavirus pneumonia (COVID-19) has raged in many countries around the world. In the process of fighting against the COVID-19, unprecedented large-scale epidemic data have been produced such as case data, spatio-temporal data, public opinion data and so on. The increasingly complex data poses a significant challenge to understand. A two-level interactive visualization system named COVID-19Vis is proposed in this paper, which collects epidemic data from multiple sources and provides an interactive mode of multi-graph linkage. Users can not only easily analyze and interpret the spatial-temporal characteristics and potential rules of the epidemic, but also find the relationship between policy, online public opinion and the development of the epidemic situation. Through a large number of visualization effects and user feedback, the effectiveness and practicability of the COVID-19Vis are further verified.

Through a set of electro-hydraulic digital valve as the core of the fuzzy control system to provide an appropriate amount of cooling fluid, the hard turning process temperature can be controlled at any set temperature. The use of the signal acquisition system based on virtual instrument, acquisition acoustic emission signals, vibration signals and temperature signals during the hard turning process. The temperature signal is processed by wavelet transform ,after vibration and acoustic emission signals be processed by wavelet packet decomposition and energy method, it is found that through providing appropriate cooling fluid can control temperatures in the processing and decrease the amplitude of vibration and acoustic emission signal, it also means that we can improve the quality of processing, and prolong the life of tool.

The heating system is an essential part of China’s urban energy system. However, the heating systems in China have varying levels of hardware and software, informationization, and intelligence. The coarse time granularity of actual system data and the missing data brings significant challenges to system analysis and diagnosis. This paper proposes to study the sample completion and granularity refinement of the time-series data of heating systems based on Wasserstein Generative Adversarial Networks (WGAN). The method is validated based on the field data from 2019 to 2020 of a district heating system in Zhengzhou. Besides, the key features affecting the quality of the completion are discussed. After introducing the data processing method of Empirical Modal Decomposition (EMD), the results show that historical data and climate data dominate the quality of the generated samples. The WGAN can generate the samples accurately. When comparing the real data under the same conditions, the accuracy rate reaches 96.4%. This study further refines the sampling granularity of the existing heating system based on the WGAN to provide more accurate samples for system analysis and diagnosis. Overall, the study provides a new data analysis method for theoretical and technical studies under data deficiency scenarios for heating systems.