Convenient life service intelligent management method and system based on big data

By clustering and modeling multi-source heterogeneous data, and combining collaborative matching networks and reinforcement learning models, the problems of data fragmentation and rigid scheduling in existing technologies have been solved, and precise supply and demand matching and dynamic resource scheduling of city-level convenient living services have been achieved.

CN122199242APending Publication Date: 2026-06-12SHENZHEN CONCEPT INFINITE TECHNOLOGY CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
SHENZHEN CONCEPT INFINITE TECHNOLOGY CO LTD
Filing Date
2026-04-24
Publication Date
2026-06-12

AI Technical Summary

Technical Problem

Existing technologies fail to effectively integrate multi-source heterogeneous data in city-level public service management, and cannot accurately capture the unique characteristics of service scenarios. This results in a simple supply and demand matching logic, a lack of dynamic adjustment capabilities in resource scheduling, and difficulty in efficiently responding to the diverse needs of residents.

Method used

By processing multi-source heterogeneous data through clustering algorithms, service scenario feature clusters are generated, and demand heat modeling and supply capacity profiling are performed. Combined with collaborative matching networks and reinforcement learning models, resource scheduling strategies are generated to achieve accurate matching and dynamic scheduling of service supply and demand.

Benefits of technology

It achieves dynamic feature capture of service scenario demand and accurate presentation of supply capacity, generates a supply and demand collaborative matching map, can adapt to changes in supply and demand, optimize resource scheduling strategies, and improve the accuracy and efficiency of service resource allocation.

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Abstract

The present application relates to big data intelligent management technical field, specifically for the method and system of intelligent management of convenient life service based on big data, including: obtaining the resident service request record, service provider supply data, service scene space-time distribution data and historical service completion feedback data in the city-level convenient service scene, forming a multi-source heterogeneous data set; the clustering algorithm is executed to the data set to generate service scene feature cluster, the demand heat modeling is carried out to the feature cluster to obtain dynamic demand heat curve, and the supply capacity portrait is carried out to the service provider supply data to obtain supply capacity vector set; the above data is input into the collaborative matching network to generate service supply and demand collaborative matching graph, the service scheduling model based on reinforcement learning is called to analyze the graph, and the output is a set of service scene resource scheduling strategies. The method realizes accurate correspondence of supply and demand and dynamic scheduling of resources, solves the problem of data fragmentation and scheduling rigidity of conventional technology, and adapts to the fine management of city-level convenient service.
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