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Remote photoplethysmography (rPPG) is a video-based heart rate measurement technology, which is widely used in special scenes where contact equipment is difficult to measure. However, the rPPG signal is very weak, and it is easily affected by factors such as uneven environmental illumination changes and the tester's head movement, which leads to the poor robustness of the existing methods in natural scenes. A deep learning model based on vision transformer is proposed to segment the facial skin region to generate the spatiotemporal feature map of the video sequence, inputs the feature map into the model for rPPG physiological feature extraction, and then fits the rPPG signal. The experiments verify the effectiveness of the method on mixed data sets and can ensure that the model has a high degree of signal fitting while significantly reducing the computational complexity of the transformer.

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Because pipeline has large pipe diameter, large throughout and high pressure, once pipeline leakage accident happens, the damage is quite serious. In addition, pipeline leakage accident caused by man-made drilling oil stolen every year results in huge economic losses on oilfield. Therefore, a real-time and accurate pipeline leak detection and location system not only can effectively decrease leakage loss and reduce the waste of manpower and material resources in patrolling work, but also is conductive to the management of oil pipeline and improvement of economic efficiency of enterprise. The paper determines leak detection and location project giving priority to negative pressure wave and supplemented by flow parameter analysis. The method not only can judge the accidence of leakage timely and accurately, but also can effectively avoid leakage false alarm caused by start or stop pumps in pipeline.