3results about How to "Fast traverse" patented technology

This application discloses a method for designing high thermal conductivity aluminum-silicon alloys based on machine learning models, relating to the field of metallurgical materials technology. The method includes: constructing an original dataset of aluminum-silicon alloys containing alloy composition, process parameters, and thermal conductivity properties; preprocessing and feature filtering the dataset to obtain key features affecting thermal conductivity; training several machine learning models based on these key features and using the coefficient of determination as a preliminary screening indicator; further screening using model performance indicators; constructing a final thermal conductivity prediction model through model fusion technology; finally, predicting the thermal conductivity of the virtual alloy composition, selecting high thermal conductivity alloy formulations based on the prediction results, and conducting experimental verification. This application solves the problems of traditional alloy design relying on trial and error, long development cycles, and insufficient prediction accuracy, achieving efficient and accurate prediction of the thermal conductivity of aluminum-silicon alloys, and providing an effective means for the intelligent design and development of high-performance thermally conductive materials.

A multi-agent context sharing method, system, and medium are disclosed, comprising: parsing the context content output by a first agent to generate corresponding metadata; associating the context content with the corresponding metadata and storing it in a distributed cache; establishing a topic set indexed by a session identifier in the distributed cache and adding the topic identifiers to the topic set; responding to a task to be executed by a second agent, obtaining the topic set corresponding to the current session identifier, reading the topic descriptions corresponding to each topic identifier in the topic set, and generating a list of topic descriptions; inputting the task to be executed and the corresponding list of topic descriptions into a large language model, receiving the topic identifier set returned by the large language model as the target topic identifiers semantically related to the task to be executed; and reading the corresponding context content from the distributed cache according to the target topic identifiers, associating the context content with the task to be executed, and inputting it into the second agent.

The application discloses a network element detection method of IPv6 network space, comprising the following steps: obtaining a list of network elements to be detected, the list of network elements to be detected recording a plurality of first network elements to be detected; performing detection on each first network element to obtain a second network element directly connected with the first network element; judging whether the second network element is an anonymous network element, if yes, using a preset probe program to find an IPv6 global unicast address of the anonymous network element based on a seed node in a preset seed set to determine network element information of the anonymous network element; repeatedly performing detection until the detection of each first network element in the list of network elements to be detected is completed; and forming a network topology of the IPv6 network space based on the network element information of each second network element. The method disclosed by the application can quickly traverse all network element devices in a sparse IPv6 network space, and quickly form a network element use condition under the IPv6 network space according to the formed table.