Zhejiang University

Xinkui Zhao

Jianwei Yin

Shuiguang Deng

Peng Hu

Meilin Liu

Chen Zhi

Xuhong Zhang

Liye Cheng

Embodiments of the present disclosure a method for dynamically generating a generative operating system based on a hardware and software environment feature, the method is configured to generate the generative operating system, and an architecture of the generative operating system includes: a hardware architecture adaptation layer and a meta kernel layer and an external library of the operating system. The method comprises: systematically and comprehensively perceiving an environment state and application requirements by an environment sensor and generating a configuration file; parsing the corresponding micro-libraries through the configuration files and obtaining a corresponding micro-libraries collection through a micro-library marketplace; assembling configured micro-libraries into the generative operating system by a system organizer; and dynamically adjusting and optimizing the generative operating system in real time by a run optimizer based on changes in a runtime environment and operational requirements.

Methods for dynamically generating generative operating systems based on hardware and software environment feature

Disclosed in the present disclosure is a low-cost and zero-shot online log parsing method based on a large language model, including: firstly, extracting content of a log in a log message using regular expressions, then, performing regular expression matching with a log template in a database; if the matching is successful, updating a log sample corresponding to the log template; if the matching fails, conducting a dialogue with the large language model to obtain a new log template; performing template correction to prevent the log template generated by the large language model from being incapable of performing regular expression matching with the log message; performing template merging when a new template is generated; performing template splitting when the log sample is updated; and for all log templates to be added to the database, firstly, normalizing the log templates by post-processing, and then storing the log templates to the database. The log template generated by the present disclosure outperforms conventional methods in terms of word parsing accuracy, with significant advantages in speed and cost compared with the direct use of the large language model to perform log parsing tasks.

Low-cost and zero-shot online log parsing method based on large language model

Growing community of inventors

Hangzhou, China

Xinkui Zhao