3 results about "Inductive analysis" patented technology

This invention discloses a method for reconstructing battery swapping station operation data based on adaptive truncation and feature correlation repair, belonging to the field of data reconstruction technology. First, a standardized two-dimensional spatiotemporal discrete model is constructed, and sequence mapping technology is used to align the discrete data stream to the date-time period operation space. Second, a zero-first truncation model based on the continuity of frequency distribution is established to adaptively remove outliers and noise. Then, Latin hypercube sampling and inverse cumulative distribution function transformation algorithms are introduced to fill in missing values ​​while ensuring that the reconstructed data conforms to the original probabilistic characteristics. Finally, the battery swapping frequency-electricity data is dimensionally reconstructed to facilitate the inductive analysis of battery swapping behavior characteristics. Numerical examples demonstrate that the method proposed in this invention effectively restores the statistical usability of the data, fully revealing the bi-peak tidal characteristics of battery swapping load and the distribution law of low-electricity anxiety, providing relatively reliable data support for load forecasting and regulation.

This invention relates to the field of automated testing. To improve the efficiency and accuracy of test result analysis, it provides a method and system for automated test result summarization and analysis. By extracting error descriptions from semantic test results and combining them with domain mapping, the error data is transformed into structured information. Then, JSON examples are constructed based on error description classification. Using a large language model, standardized prompt words are used to summarize patterns and analyze root causes of various errors. Finally, structured results containing error indexes and root causes are output. This process significantly improves the automation and efficiency of error analysis, ensures the comprehensiveness and accuracy of error coverage, and can be flexibly adapted to new application scenarios by updating domain configuration files and prompt word templates.

The application belongs to the technical field of large language model optimization, and discloses a large data driving-based large language model optimization method and system. The method comprises the following steps: inputting public text data into a test model to obtain test reply data, and calculating the test deviation between the test reply data and the reply data; when the test deviation meets a preset correction trigger condition, corresponding correction text data and correction reply data are identified; data supplement processing is performed based on the correction text data and the correction reply data to generate supplement data for model optimization; the data supplement processing comprises similarity comparison of the correction text data to divide the correction text data into data groups, generation of error registration data, and performance of a supplement operation based on a statistical analysis result. Through inductive analysis of the deviation and quality control of the supplement data, the application improves the reply accuracy, logical consistency and robustness of the target language model.