3 results about "Stroke Type" patented technology

The utility model provides a stroke type weight sensor, which comprises a shell used for being connected with a fixed platform and an inhaul cable used for being connected with a movable platform moving relative to the fixed platform, and further comprises an induction column used for inducting the stroke of a sliding block, and the induction column is vertically and fixedly arranged in an induction cavity reserved in the shell. The sliding sleeve is assembled on the induction column in a sliding manner; the sliding sleeve is linearly restrained outside the induction column and can linearly move in the induction cavity along the straight line of the induction column; wherein the induction column is a columnar induction capacitor, the fixed end of the inhaul cable is fixed to the fixed platform, the movable end of the inhaul cable penetrates out of the shell, the gravity direction of the sliding sleeve coincides with the extending path of the inhaul cable, and when the inhaul cable is pulled, the sliding sleeve synchronously moves on the induction column. All parts in the device act cooperatively, so that the stability and accuracy of the detection process are ensured, the interference of the external environment can be effectively resisted, and the service life of equipment is remarkably prolonged.

1. The name of the design product: rotary electric claw (translational large stroke type). 2. The use of the design product: for grabbing or rotating the grabbed material. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view 1.

The invention relates to the technical field of intelligent medical assistance, in particular to a method, device and equipment for assisting stroke assessment and a medium, and the method comprises the steps: receiving a brain image, clinical vital sign data, pre-hospital first-aid records and historical health data; the method comprises the following steps of: extracting deep semantic features of a brain image by adopting a convolutional neural network and a Swinin-Transform hybrid encoder; a gradient boosting tree feature encoder is adopted to extract nonlinear correlation features among the clinical vital sign data, the pre-hospital emergency records and the historical health data; based on the deep semantic features and the nonlinear correlation features, adopting residual connection and a multi-head attention mechanism to obtain complementary fusion features; determining the stroke type based on the fusion features; and when ischemic stroke is determined, the volume of a core infarction region is measured and calculated, so that the assessment time of the emergency stroke is shortened.