5 results about "Execution model" patented technology

This application provides a method and apparatus for updating a semantic codec model. The method includes: obtaining the current data distribution drift intensity for evaluating the performance degradation of the semantic codec model; calculating the current model's performance degradation rate based on the correlation function between the drift intensity and the model performance degradation rate; constructing an average drift-aware model age expression based on the model performance degradation rate and the queuing model type; calculating the relative computational load based on the drift intensity; calculating the total service time for a single model update based on the relative computational load; and periodically executing the model update steps until the update termination requirements are met, at which point execution stops. The model update steps include: solving a preset objective function to obtain an optimal update frequency; and updating the current model at the optimal update frequency to obtain the updated semantic codec model. This application enables dynamic adjustment of the update frequency, improving system resource utilization efficiency while ensuring semantic reconstruction performance.

The application discloses a natural language operation method and system of architecture modeling software, and belongs to the technical field of architecture modeling and artificial intelligence. In view of the problem that existing architecture modeling relies on graphical interface operation and natural language requirements are difficult to be directly converted into modeling execution actions, the application abstracts the operable ability of the architecture modeling software into a named tool set, generates a machine-readable tool mode list, constructs a multi-role message chain with a calling identifier, inputs the message chain and the tool mode list into an external natural language model for reasoning, analyzes and executes the structured tool call output by the model by a unified tool executor, backfills the execution result into the message chain for iterative reasoning, and realizes automatic mapping of natural language requirements to architecture modeling operations. The application can adapt to language models with different capability levels, and improves the executability and stability of natural language driven architecture modeling.

A method is presented that facilitates training a very large number of machine learning execution models for detecting anomalies in computing operations. The models are grouped together according to model type and assigned to different pods of a computing environment used to perform the monitored operations. Initial training of the models in a group is performed while monitoring resource usage, and based on the resource usage, a particular pod is selected for further training. The pod selected for training preferably has minimal resource usage variation before and after the initial training. A different pod can be selected to score the trained models. The pod selected for scoring preferably has the greatest resource usage among all containers during the initial scoring.

This invention discloses a method, apparatus, electronic device, storage medium, and program for operator hybrid compilation. The method includes: obtaining a first original operator code file of a first hardware instruction execution model and a second original operator code file of a second hardware instruction execution model; wherein a function call relationship exists between the first and second original operator code files; compiling the first and second original operator code files to obtain a first virtual instruction file and a second virtual instruction file; and linking and compiling the first and second virtual instruction files using a virtual instruction compiler to obtain an executable file of hardware instructions. The technical solution of this invention can improve the compatibility of operator hybrid compilation, reduce the maintenance difficulty of operators, simplify the underlying hardware scheduling and optimization logic, and enhance the versatility and stability of hardware resource utilization.

The application provides a large model-based multi-agent scheduling data analysis method, system, device and medium, the method comprises the following steps: receiving a natural language query input by a user, and analyzing the natural language query into a structured analysis target based on a large language model; decomposing the structured analysis target into a plurality of atomic tasks with a dependency relationship based on a data analysis knowledge graph, selecting a small model and initial parameters that meet a preset adaptation threshold in terms of task type and data characteristics, and generating a task execution plan; scheduling the corresponding small model to execute the atomic tasks according to the task execution plan, evaluating the intermediate results based on a preset quality evaluation index, dynamically adjusting the model parameters or switching the execution model for optimization according to the evaluation results, and outputting the optimization results; integrating the optimization results of the atomic tasks, performing consistency verification, and generating a final analysis report, so as to solve the problems of the prior art in terms of planning reliability, tool management complexity and autonomous adaptation capability.