Energy saving and carbon reduction schemes for hospital with photovoltaic power generation and system upgrading technology.

In order to help China achieve the double carbon target of total carbon peak and high-quality sustainable economic development, and to enrich the work and content of energy conservation and emission reduction in the building sector, the most complex and energy-consuming hospitals are taken as the key projects for energy conservation and emission reduction to carry out feasibility studies. The reasons for the high energy consumption of the existing hospital buildings were analysed, and it was proposed to upgrade the existing systems (including air conditioning, hot water system and intelligent control system) and to generate photovoltaic power for the existing buildings, taking into account the characteristics of the local climate. The results of the study showed that the energy saving and emission reduction effect of the hospital was obvious after the programme was adopted.

Interpretation of Electrocardiogram Heartbeat by CNN and GRU.

The diagnosis of electrocardiogram (ECG) is extremely onerous and inefficient, so it is necessary to use a computer-aided diagnosis of ECG signals. However, it is still a challenging problem to design high-accuracy ECG algorithms suitable for the medical field. In this paper, a classification method is proposed to classify ECG signals. Firstly, wavelet transform is used to denoise the original data, and data enhancement technology is used to overcome the problem of an unbalanced dataset. Secondly, an integrated convolutional neural network (CNN) and gated recurrent unit (GRU) classifier is proposed. The proposed network consists of a convolution layer, followed by 6 local feature extraction modules (LFEM), a GRU, and a Dense layer and a Softmax layer. Finally, the processed data were input into the CNN-GRU network into five categories: nonectopic beats, supraventricular ectopic beats, ventricular ectopic beats, fusion beats, and unknown beats. The MIT-BIH arrhythmia database was used to evaluate the approach, and the average sensitivity, accuracy, and F1-score of the network for 5 types of ECG were 99.33%, 99.61%, and 99.42%. The evaluation criteria of the proposed method are superior to other state-of-the-art methods, and this model can be applied to wearable devices to achieve high-precision monitoring of ECG.