Method for intelligent matching of resources based on metaverse and metaverse system

By constructing a virtual-real data association graph and using NLP technology, combined with reinforcement learning algorithms, the problem of resource fragmentation and difficulty in matching personalized needs in the cultural tourism industry has been solved. This has enabled intelligent scheduling and combination of resources, enhancing the immersive experience of cultural tourism and improving resource utilization.

CN122412971APending Publication Date: 2026-07-17
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Filing Date
2026-02-26
Publication Date
2026-07-17

AI Technical Summary

Technical Problem

In the cultural tourism industry, the fragmentation of cultural resources and the personalization of tourists' needs are difficult to match, and traditional services cannot respond in real time to the personalized needs in the virtual-real integration scenario of the metaverse.

Method used

By constructing a virtual-real data association map, performing semantic analysis based on NLP technology, and combining reinforcement learning and decision tree algorithms, cultural and tourism resources are dynamically matched to achieve intelligent scheduling and combination of resources.

Benefits of technology

It has enabled cross-scenario integration of resources, improved resource utilization and cultural adaptability, reduced resource waste and tourist waiting time, and enhanced the immersiveness and cultural gain of the cultural tourism experience.

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Abstract

This invention provides a resource intelligent matching method and system based on a metaverse. It constructs a virtual-real data association graph by collecting multi-dimensional data; performs semantic analysis on tourist demand data to generate text feature vectors and obtain target tasks; analyzes resource status based on scene state and cultural resource data to construct a fragmented resource graph; configures fragmented resource combination rules according to the obtained target tasks, and selects fragmented resources that meet the target tasks from the fragmented resource graph to combine them into a resource block set; calculates the similarity between the text feature vectors and the resource block set, and dynamically selects resources that meet preset constraints from the resource block set to obtain resource matching results; and generates corresponding scheduling instructions based on the resource matching results. By accurately analyzing tourist needs, constructing a fragmented resource graph, and dynamically matching cultural and tourism resources according to constraints, it is possible to achieve intelligent matching of cultural and tourism resources based on tourist needs, thereby improving tourist satisfaction.
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