6results about How to "Realize intelligent generation" patented technology

This application relates to the interdisciplinary field of artificial intelligence and reliability systems engineering, and provides a method and apparatus for intelligent generation of equipment fault behavior logic models based on LLM (Limited Linear Modeling). This method combines a vector database to achieve enhanced retrieval, constructs an agent-based framework for generating equipment fault behavior logic models, and then generates structural elements in the agent for each business component involved in the equipment fault behavior logic. Based on the model structure, the results are integrated and merged, ultimately achieving a complete and runnable equipment fault behavior logic simulation model. This method integrates the interactive modeling capabilities of agents with the semantic understanding and logical reasoning capabilities of large language models, enabling intelligent generation of fault behavior models for complex equipment and effectively improving the generation efficiency of equipment fault behavior models.

This invention discloses a method and system for automated generation and optimization of database table structures. The method specifically includes: receiving the user's backend project source code; extracting a semantic information network graph related to data persistence through static code scanning; inputting the semantic information network graph into an intelligent parsing engine to analyze code logic relationships and obtain index recommendation strategies and potential table structure design flaws; based on the index recommendation strategies and potential table structure design flaws, identifying sensitive fields and recommending encryption or de-identification schemes to obtain a first table structure; performing automated stress testing on the first table structure and adjusting the index strategy and partitioning scheme based on the test results; and optimizing the first table structure according to the adjusted index strategy and partitioning scheme to obtain a second table structure. This invention achieves intelligent generation and optimization of database table structures, improving development efficiency, enhancing maintainability, improving user experience, and ensuring data security.

The invention discloses an operator fusion model training method and apparatus, a device, a storage medium and a program product. The method comprises the steps of obtaining performance data of an operator fusion model in a historical training process; wherein the performance data comprises at least one of operator operation performance data and operator calculation structures corresponding to different operator combinations; preprocessing the performance data, and obtaining key feature data from the preprocessed performance data; carrying out updating training on the operator fusion model by utilizing the key feature data; in the updating training process, adjusting and optimizing hyper-parameters of the operator fusion model to obtain a trained operator fusion model; wherein the operator fusion model is used for predicting the performance of the operator fusion strategy. By adopting the embodiment of the invention, the problem of overhigh memory occupation during model training can be avoided, the normal operation of model training is ensured, and the model training efficiency is improved.

The present application relates to the technical field of the integration of artificial intelligence and business intelligence, and particularly relates to a ternary dynamic growth method and system based on AI intelligence driving, which comprises three modules of value potential, system kinetic energy and growth efficiency linked and configured through a ternary intelligence driving interaction interface, uses an AI algorithm containing a nonlinear coupling term to calculate a comprehensive growth index in real time, and combines multi-source data initialization, simulation stress testing, senior management intervention, online learning and a closed-loop feedback mechanism to realize adaptive optimization and dynamic evolution of enterprise growth strategies; the present application can break through the static and fragmented limitations of traditional business growth mode design, and improve the effect, intelligence and self-iteration of the growth path.

The application belongs to the technical field of file storage, and particularly relates to a file intelligent storage allocation method and system, which comprises the following steps: constructing a joint knowledge base composed of a file standard storage process sub-base, a historical storage scene information sub-base and a storage device resource information sub-base; constructing a demand depth optimization reasoning model, and performing deep retrieval on the knowledge base by using a fast association index; acquiring file and scene data in real time, and calculating the current environment safety level and file damage risk probability by using a safety evaluation model; based on the above evaluation results, generating an initial evaluation score and a storage strategy by using a comprehensive evaluation model; performing secondary matching retrieval by using the reasoning model to obtain an optimized storage allocation strategy; and finally dynamically adjusting storage resource allocation according to the optimized storage allocation strategy and real-time device state; the application realizes intelligent generation and adaptive adjustment of file storage strategies, and improves storage efficiency and safety.