Social learning method for smart city

Abstract

The invention discloses a socialized learning method for a smart city. The method comprises the following steps: constructing a layered socialized learning system; establishing a task evaluation model based on deep reinforcement learning, and optimizing the task evaluation model by using task states and channel states of all the Internet of Things devices to obtain a basic decision; the edge server utilizes federal learning edge aggregation to receive the task evaluation model, and optimizes the task evaluation model on the edge server according to the basic decision to obtain a high-level decision; the edge server uses transfer learning to guide a model in the Internet of Things equipment; and the cloud server aggregates the received task evaluation model by using federal learning cloud, formulates a municipal decision according to a high-level decision and the task evaluation model on the cloud server, and guides the task evaluation model on the edge server by using transfer learning. According to the method, federal learning is utilized to improve cooperation among intelligent agents in the layers, transfer learning is utilized between the layers to realize guidance from the upper layer to the lower layer, and the performance of the model is improved.

Description

technical field [0001] The invention belongs to the technical field of big data, and in particular relates to a smart city-oriented socialized learning method. Background technique [0002] The population explosion, resource imbalance, traffic congestion and other worsening trends in the recent urbanization process have put forward increasingly high requirements for citizens' high-quality life. With the unprecedented prosperity of 5G, Internet of Things (IoT) and Artificial Intelligence (AI), smart cities are becoming a new trend in urban development. With the popularity of smart cities, the amount of data from IoT devices will increase dramatically in 2021, reaching 850 Zettabytes. Billions of IoT devices are connected to smart cities to build various smart small areas for the entire city. These IoT devices are usually deployed with moderate computing power, such as smart street lights, smart traffic lights, smart surveillance cameras, and smartphones. Furthermore, the c...

AI Technical Summary

Problems solved by technology

1) Due to the strict delay requirements of smart cities, decisions on spectrum access and computing allocation need to be made in advance with high precision. These requirements have spawned a lot of research on AI benefit strategies; 2) Building intelligent decision-making Traditional artificial intelligence methods usually rely on providing massive data and training on one or several cloud servers. These problems further exacerbate problems such as bandwidth costs and time efficiency; 3) Edge intelligence includes learning intelligence from one or several A cloud server is pushed to the edge of the network, but they ignore the collaborative characteristics between edge servers, resulting in inefficient learning resources and even a decline in learning performance; 4) behind the operation of smart cities, there is an obvious social hierarchy, which consists of Composition of IoT devices, edge servers that determine the operation of IoT devices, and cloud servers that determine the operation of edge servers

Existing edge intelligence also ignores social decision-making in smart cities

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