Large model parallel interaction method and system based on tree topology structure

Through the tree-like topology structure and multi-task parallel interaction method, the problems of information fragmentation, single expansion dimension and interaction efficiency bottleneck in large model interaction are solved, and efficient and intelligent knowledge management and navigation are achieved.

CN120653737APending Publication Date: 2025-09-16HAINAN SIMAI TECHNOLOGY CO LTD
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Patent Information

Application Number
CN202510740480.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-06-04
Publication Date
2025-09-16

AI Technical Summary

Technical Problem

Existing large-scale model interaction technologies have problems such as severe information fragmentation, single expansion dimension, low resource utilization, inability to quickly locate historical content, and bottlenecks in interaction efficiency.

Method used

A tree-like topology is adopted, and each round of interaction between the user and the large model is defined as a node. Child nodes are generated through the knowledge point extractor to construct a tree-like topology structure. It supports multi-task parallel interaction. Users can initiate requests in parallel between different nodes, and the system processes tasks of each node asynchronously.

Benefits of technology

It achieves efficient integration and absorption of information, supports vertical deepening and horizontal expansion, optimizes resource utilization, improves interaction efficiency, reduces information omissions and computing burden, and provides intelligent contextual navigation.

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Abstract

The invention provides a large model parallel interaction method and system based on a tree topology structure, and relates to the technical field of artificial intelligence interaction.The method comprises the steps that each round of interaction between a user and a large model is defined as a node; according to the large model answer of the current node, a plurality of child nodes are generated through a knowledge point extractor, and a tree topology structure is constructed; when a user triggers any node, recursively tracing a path from the node to a root node, and aggregating node data according to a path sequence to generate a precise context; initiating a question to a large model based on the context, writing an answer into a current node and automatically generating a new child node; a user is supported to initiate interaction requests in parallel among different nodes, and the system asynchronously processes tasks of all the nodes. According to the technical scheme of large model parallel interaction based on a tree structure, scattered chat records are changed into a knowledge map which can be unfolded at will, details can be dug deeply in the longitudinal direction, different topics can be compared transversely, and multi-node simultaneous propulsion can be achieved.
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