Smart community metadata interaction method and system based on edge computing framework

Through the layered caching and dual-channel scheduling network of the edge computing framework, the problems of resource scheduling delay and inaccurate matching are solved, and efficient utilization and accurate matching of smart community resources are achieved.

CN120803756AActive Publication Date: 2025-10-17HANGZHOU ZHIMA IOT TECH CO LTD
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Patent Information

Application Number
CN202511323742.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-09-17
Publication Date
2025-10-17
Estimated Expiration
2045-09-17

AI Technical Summary

Technical Problem

Under highly dynamic and large-scale computing power demands, existing technologies have resource scheduling delays and insufficient flexibility, making it difficult to accurately match task requirements with resource status, resulting in resource waste and processing bottlenecks.

Method used

Based on the edge computing framework, it manages end, edge, and cloud data through hierarchical caching, builds a resource semantic graph, and adopts a dual-channel hybrid scheduling network to accurately match resources and tasks, including hierarchical rolling cache, time mapping, expandable label slots, and a multi-factor weighted scoring engine, combined with graph neural networks and long short-term memory networks for intelligent matching of resources and tasks.

Benefits of technology

It reduces network overhead and access latency, eliminates data heterogeneity, improves the pertinence and efficiency of task processing, and achieves efficient utilization and precise matching of resources.

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Abstract

The invention relates to the technical field of smart community data management, in particular to a smart community metadata interaction method and system based on an edge computing framework, and the method comprises the steps: carrying out the dynamic storage of multi-dimensional data through a hierarchical rolling cache; generating a uniform resource vector through sequential progressive mapping; community semantic tags are added for the uniform resource vectors through extensible tag slots, connection is established according to association rules, and a resource semantic graph oriented to community services is constructed; receiving a resident task request, constructing a multi-factor weighted scoring engine based on the resource semantic graph, and generating a task data packet with a priority label; a two-channel hybrid scheduling network is constructed, a first channel outputs resource availability and node association degree, and a second channel outputs time sequence characteristics and load prediction parameters; and fusing dual-channel output to obtain task-node correlation and generate an optimal matching strategy. According to the method, task collaboration and resource adaptive management are realized through semantic resource modeling and hybrid scheduling.
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Citation Information

Patent Citations

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    CN119065852A

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