Business process processing method and system based on large language model

By building parameter optimization of multi-relationship networks and large language models, the problem that traditional methods are difficult to capture the complex relationships of power grid services is solved, and the intelligent and automated processing of power grid business processes is realized.

CN119003704BActive Publication Date: 2025-01-14CHENGDU POWER SUPPLY COMPANY OF STATE GRID SICHUAN ELECTRIC POWER
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Patent Information

Application Number
CN202411440853.7
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-10-16
Publication Date
2025-01-14
Estimated Expiration
2044-10-16

AI Technical Summary

Technical Problem

Traditional manual processing and data processing methods are difficult to fully capture the complex relationships, diversity, heterogeneity, and dynamics in power grid business processes, resulting in inaccurate and comprehensive processing results.

Method used

Using a business process processing method based on a large language model, a multi-relationship network of power grid business description content is constructed, session events and dependencies are extracted, embedded representations and vector fusion is performed, sample network member vectors are generated, and parameters are made to initialize the large language model. Optimize and learn to generate target large language models.

Benefits of technology

It significantly improves the intelligence and automation level of power grid business process processing, realizes accurate and efficient business process processing, and can better adapt to the dynamic changes of power grid business.

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Abstract

The present application provides a business process processing method and system based on a large language model, which relates to the field of artificial intelligence technology. By constructing a multivariate relationship network of power grid business description content, the method can accurately capture power grid business conversation events and their dependencies, and provide a comprehensive data basis for the analysis of complex power grid business. Furthermore, through embedded representation and vector fusion technology, business events, linkage events and conversation knowledge labels are converted into vector representations in high-dimensional space, which effectively integrates multi-dimensional information and enhances the model's ability to understand power grid business scenarios. The sample network member vectors are used to perform linkage parameter adaptation optimization and parameter learning on the initialized large language model. The target large language model finally generated is not only highly accurate, but also can better adapt to the dynamic changes of power grid business, realizing the precision, efficiency and intelligence of business process processing.
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