6 results about "Problem determination" patented technology

The application provides a process automation and exception self-recovery system and method, wherein the system comprises: a rule engine configuration subsystem configured to configure a rule engine of a process platform; an exception problem determination subsystem configured to determine an exception problem in process approval according to characteristic information of historical process handling exception problems, and obtain an exception problem determination result; and an exception self-recovery subsystem configured to automatically handle the exception problem according to the exception problem determination result and an automatic handling rule configured in the rule engine. The process automation and exception self-recovery system and method of the application configure the rule engine, automatically identify and handle exception conditions in a process execution process, introduce a deep learning algorithm, combine characteristic information of historical process handling exception problems, fully consider possible problems in process approval, automatically calculate and determine exceptions in process approval by using the combination of the deep learning algorithm and the rule engine, and make process exception detection more comprehensive and intelligent.

To provide an information processing device, an information processing method, and an information processing program that can provide appropriate answers to questions. [Solution] The information processing device according to the present invention comprises a reception unit that receives questions from a user, a determination unit that determines which answer generation agent matches the question from among a plurality of answer generation agents, each of which has learned to provide answers based on different answer policies and is linked in a hierarchical structure for each predetermined event, and a generation unit that generates an answer to the question using the determined answer generation agent.

The invention discloses a cross-domain problem processing method and device based on a large language model, equipment and a medium. The method comprises the steps that a target problem of a target domain is received; performing similarity matching on the target problem and a source problem of at least one source field in an example pool, wherein the example pool stores source problems classified according to industries or technical fields, corresponding thinking chains and source data source fields on which thinking chain reasoning depends; based on the matching result, obtaining or generating a thinking chain associated with the target question; identifying a source data source field on which the obtained or generated thinking chain associated with the target question depends, and determining a corresponding target data source field for the target question based on the semantic similarity; constructing an enhancement instruction based on the obtained or generated thinking chain, the source data source field and the target data source field; and inputting the enhancement instruction into the large language model to generate an answer to the target question. According to the method, the big language model can generate answers which are logically coherent and reliable in data support.

This invention discloses a multi-agent collaborative reasoning system and method. The system includes: a dynamic policy coordination module for analyzing the original problem input by the user and determining initial policy parameters; a multi-round reasoning module for sending the original problem and initial policy parameters to a pool of agents, obtaining reasoning data from each agent, performing multiple rounds of interaction until round control conditions are met, and determining collaborative reasoning data based on the latest reasoning data; obtaining updated reasoning data from each agent during each round of interaction; a parameter optimization module for calculating trajectory scores on the reasoning data from the previous round for each agent, determining the current interaction mode and current interaction parameters, and updating the trajectory score weights; and a communication optimization module for processing the reasoning data from each agent and sending it to other agents. This invention enables multi-agent collaboration that organically integrates efficient interaction, deep reasoning evaluation, and optimal result selection.

The application provides a project problem determination method and device, electronic equipment and storage medium. The method comprises the following steps: designing a test case based on the requirement of a target project, and determining the total number of problems of the target project; determining the problem omission rate of the target project up to the current release number of the target project based on the total number of problems; determining the problem omission number of the target project based on the problem omission rate of the target project and the total number of discovered problems of the target project; detecting whether the problem omission number of the target project meets a preset problem omission number threshold value; if yes, allowing the target project in the current release number to be used; and if no, continuously determining the problem omission number of the target project in the next release. The problem omission number of the project is dynamically predicted according to the effectiveness of the project problem solution. The predicted problem omission number can be used to evaluate the project quality, thereby improving the maturity of the project.