Evaluation of the Application Effect of Intelligent Community Service Platform in Grassroots Governance
This article designs an intelligent community service platform,first using a distributed framework to build the platform and provide data services for the intelligent community.Secondly,an intelligent machine learning model is adopted to accelerate network training for community data classification tasks.A high-precision association mechanism based on multi-source heterogeneous big data is used to store different types of topic data in corresponding topic libraries,achieving the fusion processing of structured,semi-structured,and unstructured data.Finally,four types of account information are set up,including platform administrators,community government officials,community service units,and community residents,to provide services for community residents.The simulation results show that the functional test results of the intelligent community service platform have all passed the test,and the maximum response time change is less than 1%,proving the feasibility of the simulation application of the intelligent community service platform in grassroots governance.
Community Service PlatformDistributed FrameworkMachine LearningData ClassificationResponse Time
万方数据
维普
