2results about How to "Improve approval efficiency" patented technology

The invention discloses a power communication maintenance ticket approval method and system. The method comprises the steps of obtaining to-be-approved electric power communication maintenance ticket data; performing semantic understanding and information extraction on to-be-approved electric power communication maintenance ticket data by using a neural network module, and outputting structured semantic information; inputting the structured semantic information into a symbol reasoning module, performing logical reasoning and compliance verification by the symbol reasoning module based on a differentiable symbol rule base, and outputting an approval result; the method comprises the following steps: converting an electric power communication maintenance ticket approval rule into a symbolic logic expression with a weight by adopting a differentiable logic operator, and constructing a differentiable symbolic rule base; and constructing a double-layer optimization framework, and jointly optimizing parameters of the neural network module and weights of rules in the differentiable symbol rule base based on upper-layer approval task loss and lower-layer symbol rule logic consistency loss. According to the method, the examination and approval efficiency, accuracy and auditing performance are remarkably improved, and the method is suitable for intelligent processing of power grid communication maintenance business.

This invention discloses a method for recommending official document approval opinions based on semantic matching and role awareness, comprising the following steps: S1. Constructing a historical document vector database and a structured metadata database; S2. Obtaining documents to be approved and performing feature extraction and preliminary screening; S3. Performing text block-level similarity retrieval on the documents and aggregating them; S4. Calculating job matching scores based on job information; S5. Calculating the final recommendation score and sorting the documents in descending order based on the final recommendation score, outputting the top 5 with the highest scores as the recommended results. This invention improves recommendation accuracy by using a pre-trained semantic embedding model to understand the deep semantics of document content. Furthermore, through job matching score calculation and approval node position alignment mechanisms, it deeply binds the recommended approval opinions to specific approval positions and approval process positions, achieving personalized recommendations.