Self-learning correction method, system and equipment for hotel knowledge base and medium

By updating the hotel knowledge base in real time through multimodal analysis and incremental learning models, we can solve the problems of low maintenance efficiency of traditional knowledge bases, difficulty in structuring implicit knowledge, and defects in AI customer service systems, and achieve automated iterative optimization and accuracy improvement of the knowledge base.

CN120783750APending Publication Date: 2025-10-14北京云迹科技股份有限公司
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Patent Information

Application Number
CN202511002997.9
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-07-21
Publication Date
2025-10-14

AI Technical Summary

Technical Problem

Traditional knowledge bases are inefficient to maintain, implicit knowledge is difficult to structure, static knowledge bases cannot adapt to dynamic policies, existing AI customer service systems have high speech recognition error rates, cannot associate historical call information, and lack knowledge self-verification capabilities.

Method used

Through multimodal analysis models and incremental learning models, the voice content of customer calls is analyzed in real time, the knowledge base is automatically updated, and the automatic iterative optimization of the knowledge base is achieved by combining semantic understanding and dynamic correction technology of the knowledge base.

Benefits of technology

It shortens the knowledge update cycle, improves the timeliness and accuracy of the knowledge base, improves the knowledge structure, ensures that the answers provided by AI customer service meet the latest business requirements, and improves the accuracy and compliance of customer service.

✦ Generated by Eureka AI based on patent content.

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Abstract

The invention relates to the crossing field of artificial intelligence and hotel informatization management, in particular to a hotel knowledge base self-learning correction method, system and device and a medium. The method comprises the following steps: acquiring a client call voice stream, and preprocessing to obtain voice data; inputting the voice data into a preset multi-modal analysis model and an incremental learning model to generate a knowledge correction decision; updating a knowledge base version and an AI response generator based on the knowledge correction decision, and obtaining customer satisfaction feedback; and adjusting the knowledge correction decision through the feedback. And a knowledge correction decision is automatically generated by using a multi-modal analysis model and an incremental learning model, and a knowledge base version is quickly updated based on the decision. By means of the process, automatic correction of knowledge is achieved, the knowledge updating period is greatly shortened, the knowledge base can reflect latest service information and customer requirements in time, and the timeliness and accuracy of the knowledge base are improved. By incorporating the implicit knowledge into the knowledge base, the knowledge base is richer and more comprehensive.
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Citation Information

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  • Automatic filling and examination and knowledge base integrated optimization method based on dynamic rule engine

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  • Brassica oleracea knowledge dynamic expression and interaction method and system based on multi-modal fusion

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