Multi-model inference chain data analysis method and device based on natural language input, medium and program product

By constructing a multi-model inference chain and introducing a large language model scheduling hub, the problems of semantic parsing and full-process automation in data analysis were solved, realizing an efficient and transparent data analysis process, and improving the credibility of analysis results and the adoption rate by managers.

CN121503660APending Publication Date: 2026-02-10BEIJING NEUSOFT VIEWHIGH CO LTD
0 Cites 2 Cited by

Patent Information

Application Number
CN202511564098.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-10-30
Publication Date
2026-02-10

AI Technical Summary

Technical Problem

Existing technologies lack semantic parsing capabilities, multi-model collaborative reasoning capabilities, and fully automated execution in data analysis, resulting in low analysis efficiency, inconsistent results, and opaque processes.

Method used

By employing a multi-model inference chain data analysis method based on natural language input, we utilize neural network models to analyze user needs, construct a multi-model inference chain, introduce a large language model as a scheduling hub, record and display the analysis process, and achieve an automated closed loop from data acquisition to result generation.

Benefits of technology

It improves the consistency, controllability, and quality of data analysis, ensures the transparency and verifiability of the analysis process, reduces the workload of manual configuration and maintenance, and increases the trustworthiness and adoption rate of analysis conclusions.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The invention discloses a multi-model inference chain data analysis method and device based on natural language input, a medium and a program product. The method comprises the steps that an analysis problem text in a natural language form is analyzed into a semantic task structure, and the semantic task structure is mapped into a structured analysis model conforming to a preset specification; registering the structured analysis model as an instance to an analysis model library, allocating a unique identifier ID to the structured analysis model during registration, and writing attribution relation information of the structured analysis model; an upstream model ID and a downstream model ID in the attribution relation information of the instance are read, the attribution relation information of the upstream model and the downstream model is analyzed, layer-by-layer expansion is carried out in the mode, and a multi-model reasoning chain of the instance is constructed according to the dependency relation between the models; and according to a sequence determined by the multi-model reasoning chain, executing each model instance in sequence or in parallel, and integrating the obtained final output into a comprehensive analysis report. According to the invention, automatic execution from a natural language to full-process data analysis can be realized.
Need to check novelty before this filing date? Find Prior Art