Abstract: A method and system for real-time calculating a microseismic focal mechanism based on deep learning is provided, which belongs to the technical field of microseismic monitoring. The method includes: creating a training dataset, the training data including simulated DAS microseismic strain data and a focal mechanism corresponding to the simulated DAS microseismic strain data; training a focal mechanism calculation model by using the training dataset, with the simulated DAS microseismic strain data as an input and the focal mechanism corresponding to the simulated DAS microseismic strain data as a target output, so as to obtain a trained focal mechanism calculation model; collecting DAS microseismic strain data by a surface and downhole DAS acquisition system; performing preprocess operations such as removing abnormally large values on the DAS microseismic strain data; inputting the preprocessed DAS microseismic strain data into a trained focal mechanism calculation model to obtain a focal mechanism.

Abstract: Embodiments of the present disclosure provide a multi-physical field imaging method based on PET-CT and DAS, comprising: wrapping distributed acoustic sensors on a surface of a non-metallic sample to be tested, and then placing them in a pressure device; loading triaxial pressures; preparing a tracer fluid; pumping the tracer fluid into the non-metallic sample; collecting PET images and CT images of internal structure of the non-metallic sample, meanwhile, monitoring internal acoustic emission events of the non-metallic sample in real time; combining the PET images with the CT images, to obtain PET/CT images; locating the acoustic emission events, and obtaining occurrence time and spatial location of internal structural perturbations; and analyzing a mechanism of fluid-solid coupling effect in the non-metallic sample under loaded stress. The imaging method and system of the present disclosure can accurately and reliably image the fluid-solid coupling process in the material.

Abstract: Provided are an energy-saving method, a base station, a control unit, and a storage medium. The method includes: collecting energy-consumption influencing factor data of a target cell; predicting a load trend of the target cell according to the energy-consumption influencing factor data; and determining, according to the load trend, an energy-saving strategy of the target cell and effective time corresponding to the energy-saving strategy and executing the energy-saving strategy according to the effective time.

Abstract: The present invention provides an aluminum wheel burr removing machine which includes a roller bed device (1), a positioning and rotating device (2) and a knife box device (3). A wheel (4) is conveyed to a specified position by the roller bed device (1), the positioning and rotating mechanism (2) clamps and rotates the wheel (4), the knife box device (3) horizontally feeds to cut burrs of the wheel (4), the roller bed device (1) conveys the wheel to next station, meanwhile, next wheel enters the roller bed device (1), and next cutting cycle is carried out. The machining time of the present invention is 25-30 seconds, so that the manpower cost is reduced, the machining efficiency is improved, and positive effects are achieved in hub burr removal.

Abstract: The present invention provides an online aluminum wheel burr removing machine which includes a roller bed device (1), a positioning and rotating device (2) and a knife box device (3). A wheel (4) is conveyed to a specified position by the roller bed device (1), the positioning and rotating mechanism (2) clamps and rotates the wheel (4), the knife box device (3) horizontally feeds to cut burrs of the wheel (4), the roller bed device (1) conveys the wheel to next station, meanwhile, next wheel enters the roller bed device (1), and next cutting cycle is carried out. The machining time of the present invention is 25-30 seconds, so that the manpower cost is reduced, the machining efficiency is improved, and positive effects are achieved in hub burr removal.