Abstract: An orbiting scroll plate includes a base including a first side and a second side, and a scroll wrap attached to the base at the first side and having a spiral cross-section. The scroll wrap extends from a first end to a second end, the first end is positioned close to the outer side of the base, and the second end is positioned close to the center of the base. A return channel connects to and is in communication with the first side and the second side, and a release channel connects to and is in communication with the first side and the second side and positioned closer to the outer side of the base than the return channel is. The scroll wrap and the base are integrally constructed. A scroll compressor containing the orbiting scroll plate is also described.

Abstract: A living body detection method and device are disclosed. Wherein the method comprises the following steps: extracting valid depth data of a target detection object from depth map data containing the target detection object; generating a depth difference histogram based on the valid depth data; and inputting the depth difference histogram into a pre-trained machine learning classifier to obtain a determination result of whether the target detection object is a living body. By adopting this method, the detection accuracy can be improved.

Abstract: A method, an apparatus and an electronic device for face liveness detection based on a neural network model are provided. The method includes: a target visible light image and a target infrared image of a target object to be detected are obtained (S101); a first face image is extracted from the target visible light image, and a second face image is extracted from the target infrared image (S102); a target image array of the target object is generated based on multiple monochromatic components of the first face image and a monochromatic component of the second face image (S103); and feeding the target image array into a pre-trained neural network model for detection, to obtain a face liveness detection result of the target object (S104).

Abstract: A living body detection method and device are disclosed. Wherein the method comprises the following steps: extracting valid depth data of a target detection object from depth map data containing the target detection object; generating a depth difference histogram based on the valid depth data; and inputting the depth difference histogram into a pre-trained machine learning classifier to obtain a determination result of whether the target detection object is a living body. By adopting this method, the detection accuracy can be improved.

Abstract: A efficient large-scale selective laser melting forming device comprises a rack (1), a forming workbench (2) arranged on the rack, a forming bin (3) sleeved on the forming workbench and a galvanometer scanning device (7) arranged outside the forming bin and moving reciprocally along the powder spreading direction to form a multi-station working state, and a smoke blowing and sucking mechanism (8) arranged in the forming bin in a mode of synchronously moving to the same station with the galvanometer scanning device. The galvanometer scanning device can move reciprocally along the powder spreading direction to form a multi-station working state. The galvanometer scanning device is entirely arranged outside the forming bin. The smoke blowing and sucking mechanism arranged in the forming bin can synchronously move with the galvanometer scanning device to the same station.

Abstract: A method, an apparatus and an electronic device for face liveness detection based on a neural network model are provided. The method includes: a target visible light image and a target infrared image of a target object to be detected are obtained (S101); a first face image is extracted from the target visible light image, and a second face image is extracted from the target infrared image (S102); a target image array of the target object is generated based on multiple monochromatic components of the first face image and a monochromatic component of the second face image (S103); and feeding the target image array into a pre-trained neural network model for detection, to obtain a face liveness detection result of the target object (S104).