Enterprise intelligent management system and method based on cloud platform

By adopting deep learning natural language processing technology in the enterprise intelligent management system, fine-grained analysis of customer feedback is solved, and the existing system cannot deeply understand the complex emotions of customers is achieved, and an accurate assessment of customer satisfaction is achieved.

CN119809706AInactive Publication Date: 2025-04-11SHENZHEN YINGFANG TECH CO LTD
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Patent Information

Application Number
CN202411762548.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-03
Publication Date
2025-04-11
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

The existing enterprise intelligent management system has shortcomings in capturing customer satisfaction, and it is impossible to deeply understand the actual feelings and specific problems of customers, especially the inability to capture the complex emotions and emotional changes of customers in situations.

Method used

The natural language processing technology and encoding method based on deep learning are used to segment and semantically encode the content of the feedback text of customers' use, and the semantic enhancement of conditional descriptions is achieved to realize intelligent judgment of the emotional polarity labels at each problem level, and determine whether the target customer is satisfied with the product.

Benefits of technology

It realizes fine-grained analysis of customer feedback, captures customer complex emotions, understands customer's actual feelings and specific problems, accurately judges customer's emotional polarity, and provides comprehensive and accurate analysis results.

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Abstract

The invention discloses an enterprise intelligent management system and method based on a cloud platform, and the method comprises the steps: extracting a use feedback questionnaire of a target customer from a questionnaire survey storage module, and extracting use feedback text content from the use feedback questionnaire; and then segmenting and semantic coding of the use feedback text content are carried out by adopting a natural language processing technology and a coding mode based on deep learning, so that condition description semantic enhancement is carried out on each coded question-reply semantic coding feature, and intelligent judgment of an emotion polarity label of each question level is realized. And determining whether the target customer is satisfied with the product. Compared with a simple scoring method, the method has the advantages that fine-grained analysis can be performed on the use feedback of the customer, so that the complex emotion of the customer can be captured, the actual feeling and specific problems of the customer can be understood, the emotion polarity of the customer can be accurately judged, and comprehensive and accurate analysis on the customer feedback can be realized.
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