2results about How to "Reduce traction" patented technology

The application discloses an integrated multi-stage control cervical vertebra traction device, which comprises a bed body, a rotatable head fixing mechanism is slidably arranged at the head position of the bed body, the head fixing mechanism comprises a movable headrest which is slidably arranged on the bed body, a supporting ring which is fixedly connected with the movable headrest, and a front supporting ring and a rear supporting ring which are rotatably connected with the two ends of the supporting ring; a traction mechanism is arranged on the bed body, the traction mechanism comprises an upper sliding block which is rotatably connected with the movable headrest and a pulling rope which pulls the upper sliding block, and the upper sliding block is slidably clamped on an upper sliding frame; the upper sliding frame is fixedly connected with a movable cylinder, the movable cylinder is slidably clamped in a positioning cylinder, and the movable cylinder and the positioning cylinder are in gear engagement or are not in contact. In the application, the problem that the traction force is difficult to control and the traction direction cannot be adjusted in the cervical vertebra operation is solved, the front supporting ring is clamped at the chin position of a patient, the rear supporting ring is fixed on the head of the patient, and the fixing and protection effects are achieved.

The present application belongs to the technical field of commodity information processing, and particularly relates to a commodity same-model identification method and system based on a large language model. The method obtains text information and image information of a to-be-built library commodity and a to-be-searched commodity, and after processing such as field segmentation, unit of measurement normalization, and abnormal character filtering, constructs a field text sequence and an image block sequence; then, in combination with field weights and field confidence masks, cross-modal alignment is performed on field text vectors, main image global vectors, and detail image local vectors to obtain fusion feature vectors and attribute sub-vectors; subsequently, same-model determination is realized through candidate rough screening, attribute difference recheck, and critical sample re-trial, and the model is updated through misjudgment sample back-feeding. The scheme can reduce the interference of noise fields and abnormal data on the identification result, and enhance the ability to distinguish commodities that are similar in appearance but different in model, specifications, or packaging quantity.