4results about How to "Universal adaptability" patented technology

The utility model discloses an integrated carrying device and an unmanned aerial vehicle. The integrated carrying device comprises a radar support, a carrying bin and a rack which are sequentially connected from top to bottom. The radar support is used for installing a radar, and the carrying bin is used for installing a holder and an airborne computer. The radar support, the carrying bin and the rack are formed by integrally cutting carbon fiber plates, and hollow weight reduction areas are arranged on the radar support, the carrying bin and the rack. The utility model has the characteristics of compact structure, high space utilization rate, high reliability and the like, realizes extreme light weight on the premise of ensuring the structural strength, and modularizes the installation of the radar, the airborne computer and the pan-tilt in the vertical space, so that the installation, debugging and replacement of various task equipment are more convenient, and the installation cost is reduced. And a universal carrying platform with high integration level and high reliability is provided for the multifunctional unmanned aerial vehicle.

The invention particularly relates to a semantic knowledge migration-based high dynamic range imaging method, which comprises the following steps of: acquiring a low dynamic range image, and generating an initial high dynamic range image by utilizing an initial high dynamic range reconstruction model; converting the initial high dynamic range image into a reconstructed image of a standard dynamic range domain through domain migration operation; processing the reconstructed image of the standard dynamic range domain by adopting a semantic segmentation network SAM to obtain semantic priori; processing the reconstructed image of the standard dynamic range domain by combining semantic prior with a semantic prior guidance reconstruction model to obtain a refined reconstructed image; based on a knowledge distillation method, cross-domain semantic priori migration is realized at three levels of content, color and features, and an initial high dynamic range reconstruction model is optimized through a combined loss function; in the reasoning stage, only the optimized initial high dynamic range reconstruction model is adopted to output a final high dynamic range image. According to the method, high-dynamic-range image reconstruction is carried out by effectively utilizing the advanced capability of semantic priori.

This invention discloses a tracked mobile carrier based on highway guardrails, comprising a carrier body, a walking drive mechanism, and a support and guiding mechanism. The walking drive mechanism includes an outer drive-clamping track assembly and an upper drive-clamping track assembly located on both sides of the corrugated guardrail, forming a bidirectional clamping drive structure. The two tracks are in surface contact with the corresponding surfaces of the corrugated guardrail to provide walking drive force. Both track assemblies are equipped with adaptive adjusting slides with pre-tension springs, enabling adaptive inward and outward floating of the driven wheel assembly to continuously maintain the track's contact with the corrugated guardrail. The support and guiding mechanism includes a top support roller and a lower limiting roller with a buffer spring, achieving vertical load-bearing and lateral limiting and buffering. This invention enables highly stable and reliable all-weather autonomous movement along highway corrugated guardrails, adapting to various complex working conditions of the guardrails and meeting diverse application needs such as highway maintenance and inspection, and traffic control.

The application belongs to the technical field of book recommendation, and particularly relates to a book recommendation method and system based on a knowledge graph. The method comprises the following steps: S1, acquiring book related data and book system user related data; S2, cleaning the book related data and the book system user related data; S3, constructing a knowledge ontology according to the cleaned book data, and generating a classified book knowledge graph; S4, constructing a knowledge ontology according to the book system user related data and user system use condition information, and generating a user knowledge ontology and a user portrait knowledge graph; and S5, generating a personalized book recommendation for a user according to the book knowledge graph, the user knowledge ontology and the user portrait knowledge graph, and combining a hybrid sequence recommendation algorithm. The application has the characteristics of combining a deep learning technology, using a knowledge graph to represent book knowledge information and user interest conditions, and being highly automated, dynamic and efficient.