5results about How to "Improve discovery efficiency" 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.

The application relates to a network space knowledge extraction method and device based on rule-enhanced prompt learning, which comprises the following steps: acquiring text data to be extracted in a network space security field and a prompt input; splitting the prompt input into a subject prompt, a relationship prompt and an object prompt; connecting sub-prompts of a conditional function related to an ontology rule by using a logical rule with an and normal form to obtain a task-specific prompt for the text to be extracted; performing network space knowledge extraction on the text data to be extracted by using the task-specific prompt and a pre-trained language model trained to output entities and relationships of a current task in the network space security field; and using the entities and relationships to determine threat intelligence data in the current network space security field. The application realizes efficient extraction of entities and relationships in the network space security field by using a prompt tuning technology, and improves the efficiency of threat intelligence data discovery and acquisition in the network space security field.

The application discloses a kind of software development cooperation method and device based on multi-agent system, it is related to software development technical field.The application is converted into structured semantic representation by demand analysis agent using deep learning and field dictionary joint identification technology, and the semantic gap of artificial analysis is eliminated;Through rule verification agent and component optimization agent collaborative work, based on dependency graph construction, rule engine verification and iterative optimization algorithm, automatically identify and correct the logical conflict between form, process, report, solve the maintenance problem of multi-component collaborative consistency;Through seven agent chain cooperation covers the whole process from demand analysis to deployment preparation, the development task that originally needs artificial serial execution is converted into the automatic flow of inter-agent, realizes the whole process automatic intelligent collaborative development from natural language demand to deployable application model, significantly improves software development efficiency and quality.

The application relates to the field of servers, in particular to a cabinet server monitoring operation and maintenance system, a cabinet server monitoring operation and maintenance method and a cabinet. The system is connected to a switch operating system, is automatically started when the system is started and starts a DHCP service. The system comprises an asset module which is used for monitoring the port state of switches in a cabinet to determine whether a server node is connected, obtaining server configuration information when the server node is found and sending a node addition message; a control module which is used for performing network configuration on the connected server node after receiving the node addition message; a monitoring module which is used for monitoring the running state of the connected server node to generate a standardized alarm object and sending the standardized alarm object by using the configured network; and an alarm module which is used for receiving the standardized alarm object and performing alarm generation, upgrading, downgrading, merging and clearing operations on the standardized alarm object to generate alarm messages of each server node. The scheme of the application realizes automatic discovery and monitoring operation and maintenance of cabinet server nodes.

This disclosure provides a method for optimizing the substituent structure in a tetrahydrofuran-based NaV1.8 inhibitor based on reinforcement learning, and the inhibitor itself. The method includes: obtaining a receptor spatial model of the NaV1.8 inhibitor and the chemical groups to be updated in the initial substituent structure; performing topological connections, group deletions, or group substitutions on the chemical groups to obtain the updated substituent structure; under the spatial constraints of the receptor spatial model, obtaining multiple conformations by changing the spatial orientation of the rotatable single bonds in the updated substituent structure, and obtaining target evaluation scores based on the conformational fit of each conformation to the receptor spatial model, the metabolic stability of each conformation, and the hydrophilicity characteristics corresponding to the updated substituent structure; updating the strategy parameters of the generation model based on the target evaluation scores until the target evaluation score is greater than or equal to a preset threshold, and then outputting the target substituent structure. This disclosure enables synergistic optimization of substituent fit, metabolic stability, and hydrophilicity / hydrophobicity balance.