2results about How to "Regulatory" patented technology

The utility model relates to the technical field of dressing devices, in particular to an anti-overflow multi-purpose liquid dressing device which comprises a charging barrel, a barrel cover is detachably fixed to the top of the charging barrel, a miniature liquid pump is fixed to the lower side of the outer wall of the charging barrel, one end of the miniature liquid pump is communicated with the lower side of the outer wall of the charging barrel, and the other end of the miniature liquid pump is fixedly communicated with a guide pipe. The outer end of the guide pipe fixedly communicates with a discharging pipe, a plurality of evenly-distributed holes are formed in the bottom of the discharging pipe, a screw penetrates through the outer end of the discharging pipe in a threaded mode, and an inner cavity of the screw is rotationally connected with a piston block matched with the discharging pipe. The dressing in the charging barrel is pumped through the micro liquid pump and guided into the discharging pipe, the dressing is sprayed out through the holes in the bottom of the discharging pipe, the screw rod can be rotated according to the wound area to drive the piston to adjust the position, the number of the discharging holes is adjusted, and therefore the dressing spraying device can adapt to the wound area to conduct accurate spraying. And the wound dressing efficiency is ensured.

A method for extracting triples from knowledge graphs in the mining industry based on ontology constraints and thought chain principles is proposed. The method constructs a highly complete mining domain ontology, acquires and preprocesses unstructured text data sources from the mining domain, and then constructs structured prompts containing ontology constraint information and thought chain reasoning instructions, transforming the triple extraction task into a constrained generative reasoning process. The structured prompts are input into a generative large language model. After receiving the input, the model, relying on its self-attention mechanism and pre-trained knowledge, strictly follows the pre-defined thought chain paths in the structured prompts to conduct reasoning. The output of the generative large language model is parsed using a parsing algorithm, separating the thought chain part and the final result part of the model output. Triple data structures are extracted from the final result part to form a candidate triple list, followed by ontology-based post-processing verification and optimization. This method enables efficient, accurate, and standardized extraction of knowledge in the mining domain.