2results about How to "Implement automatic review" patented technology

The invention discloses a work order auditing management method and system based on a large language model and a medium. The method comprises the steps of receiving input and correspondingly matching a work order auditing rule; based on the matched work order auditing rule, analyzing and processing the input through a large language model NLP, and converting the input into a natural language rule; carrying out synonym and antonym conversion on input white words, and carrying out equal rule conversion on converted words based on a corpus word library; performing rule text and code conversion on the converted natural language rule; and carding the processed rules into SQL statements which can be identified by the system according to the fields and the judger rule base. Updating the rule knowledge base; and writing the processed SQL statements which can be identified by the program into a rule knowledge base. According to the method, non-technical personnel can adjust and modify the work order information verification rule by using a natural language by using a large language model technology, so that the intelligent level of a work order system is enhanced, the development cost of the system is reduced, the auditing efficiency of a work order process system is improved, and the method has relatively good practicability.

The application relates to an intelligent content processing system based on a large model and a semantic vector, which comprises a user content preprocessing module, a user content search service module, a user content access processing module, a user content recommendation module, a user comment, opinion and dynamic classification and statistics module of a scroll bar and a user sensitive word and sentence analysis module and the like. The application also relates to a processing method. The intelligent content processing system based on the large model and the semantic vector and the method thereof adopt the application, automatically extract the abstract, the main body and the theme of the content through the large model, generate the semantic vector, combine the traditional inverted index to build a hybrid retrieval service and realize the deep fusion of keyword matching and semantic understanding. Meanwhile, the interest model is dynamically constructed based on the user behavior, various strategies are fused to realize accurate personalized recommendation, a double-layer mechanism of DFA rule and semantic vector matching is adopted for intelligent content auditing and classification, and the intelligent level, the accuracy and the system efficiency of the content processing are significantly improved.