5 results about "Experimental control" patented technology

The application provides a code reading large model parameter optimization method based on vllm deployment, which is implemented by a computer, first, the core viewpoints of a paper are combined, an experimental group is designed, a benchmark experimental group and an experimental control group are designed, experimental record data contents are used, experimental results are obtained, and mathematical analysis is combined to analyze the obtained data, then, suspected problem data is investigated and surveyed to obtain the best parameter configuration of the code reading large model relying on the vllm deployment, the core viewpoints of the paper can be combined to design reasonable benchmark experimental groups and experimental control groups, experimental record data contents are used, and mathematical analysis is combined to obtain the best parameter practice of the code reading large model relying on the vllm deployment. The application can ensure that the parameter optimization result of the code reading large model deployed on the vllm has a complete evidence chain, and has enough confidence to apply the optimized parameters in a real production environment.

The application discloses an experimental control method and device based on self-description instructions and belongs to the technical field of experimental control. The application internally arranges a plurality of different atomic instructions in a built-in action arrangement module, arranges the atomic instructions into different operation sequences according to different experimental scenes, generates different self-description instructions by means of different operation sequence sets, and reports the calling mode of the self-description instructions to a device self-description service module by the built-in action arrangement module; when an experimental workstation is connected to the experimental control system, an experimental task scheduling module queries the self-description instructions in the device self-description service module according to the type of the experimental workstation, and controls the experimental workstation to perform experimental work according to the queried self-description instructions. Therefore, the experimental control system can call the experimental workstation according to the self-defined description of various experimental workstations, effectively improves the experimental efficiency, truly realizes plug and play, realizes automatic execution of experiments, and has strong practicability and wide applicability.

The application provides an experimental control device for studying an ejector system, which comprises a high-pressure power source pipeline, a multi-stage ejector pipeline and a control and data acquisition system; the high-pressure power source pipeline is used for providing high-pressure gas for the multi-stage ejector pipeline, so that each stage of the multi-stage ejector pipeline can work normally; the multi-stage ejector pipeline comprises a first-stage ejector pipeline to an n-stage ejector pipeline, and each stage of the ejector pipeline is connected in series; and the control and data acquisition system is used for controlling the multi-stage ejector pipeline and collecting data. The experimental control device for studying the ejector system can study the performance of various ejector systems, so that the ejector system can meet the design target, adapt to various variable working conditions and improve the ejector performance.

The invention relates to the technical field of automatic experiments, and discloses an experiment control method for development of an electrochemical catalyst, which comprises the following steps: determining an experiment target and an initial parameter space, generating a first round of experiment combination based on the initial parameter space, and automatically executing liquid preparation, reaction, sample synthesis and electrochemical performance test based on the initial parameter space, obtaining first-round experiment result data; inputting the first round of experiment result data into an algorithm module, obtaining first round of analysis result data output by the algorithm module, dynamically generating a next round of experiment parameter scheme according to the first round of analysis result data, and driving the platform to execute the experiment again based on the next round of experiment parameter scheme; and repeatedly executing the step of obtaining the next round of experiment parameter scheme, judging whether a preset stop condition is met or not based on the new next round of experiment parameter scheme, if not, carrying out the next round of experiment, and otherwise, stopping the experiment. The development efficiency and precision of the electrochemical catalyst can be improved.

The invention provides a vllm deployment-based code interpretation large model parameter tuning method, which is implemented by a computer, and comprises the following steps of: firstly, designing an experimental group by combining a core viewpoint of a thesis, designing a reference experimental group and an experimental control group, recording data contents by using an experiment to obtain an experimental result, and combining mathematical analysis to obtain a mathematical analysis result; the method comprises the following steps of: analyzing obtained data, and then checking and surveying data with suspected problems so as to obtain optimal parameter configuration of a code interpretation large model deployed by relying on vllm, and by combining a core viewpoint of a thesis, designing a reasonable benchmark experiment group and an experiment control group, recording data content by using an experiment and combining mathematical analysis, determining the optimal parameter configuration of the large model. And obtaining the optimal parameter practice for interpreting the large model by relying on the codes deployed by the vllm. According to the method, it can be ensured that a complete evidence chain exists in the result of parameter adjustment and optimization of code deployment on the vllm to interpret the large model, and sufficient confidence is provided for applying the adjusted and optimized parameters in the real production environment.