The transformation of old neighborhoods involves many important types of livelihood development projects, of which smart transformation is considered one of the most important tasks at present. The smart transformation of old neighborhoods provides an important means to promote sustainable development; however, enhancing the willingness of residents to participate is an important prerequisite for the smart transformation of old neighborhoods. Considering perceptive value acceptance theory and community ownership theory in the field of information management and sociology, we study the intention of residents in old communities to participate in intelligent transformation and its influence mechanisms. Our results show that willingness to participate in the smart renovation of old residential areas is positively affected by the perceived value of smart renovation and the sense of community attachment of residents. The sense of community plays an intermediary role in the relationship between perceived value and willingness to participate in smart renovation. At the same time, the perceived value of intelligent renovation of old residential areas and a sense of community have chain mediating effects between perceived usefulness, perceived enjoyment, perceived risk, perceived cost, and willingness to participate. This paper not only achieves theoretical expansion but also provides good support for accelerating the intelligent transformation of old communities in China.

With the continuous advancement of cloud computing and satellite communication technology, the cloud-network-integrated satellite network has emerged as a novel network architecture. This architecture harnesses the benefits of cloud computing and satellite communication to achieve global coverage, high reliability, and flexible information services. However, as business types and user demands grow, addressing differentiated Quality of Service (QoS) requirements has become a crucial challenge for cloud-network-integrated satellite networks. Effective resource allocation algorithms are essential to meet these differentiated QoS requirements. Currently, research on resource allocation algorithms for differentiated QoS requirements in cloud-network-integrated satellite networks is still in its early stages. While some research results have been achieved, there persist issues such as high algorithm complexity, limited practicality, and a lack of effective evaluation and adjustment mechanisms. The first part of this study examines the state of research on network virtual mapping methods that are currently in use. A reinforcement-learning-based virtual network mapping approach that considers quality of service is then suggested. This algorithm aims to improve user QoS and request acceptance ratio by introducing QoS satisfaction parameters. With the same computational complexity, QoS is significantly improved. Additionally, there has been a noticeable improvement in the request acceptance ratio and resource utilization efficiency. The proposed algorithm solves existing challenges and takes a step towards more practical and efficient resource allocation in cloud-network-integrated satellite networks. Experiments have proven the practicality of the proposed virtual network embedding algorithm of Satellite Network (SN-VNE) based on Reinforcement Learning (RL) in meeting QoS and improving utilization of limited heterogeneous resources. We contrast the performance of the SN-VNE algorithm with DDRL-VNE, CDRL, and DSCD-VNE. Our algorithm improve the acceptance ratio of VNEs, long-term average revenue and delay by an average of 7.9%, 15.87%, and 63.21%, respectively.

According to forecast data from the State Grid Corporation of China, the number of terminal devices connected to the power grid is expected to reach the scale of 2 billion within the next five years. With the continuous growth in the number and variety of terminal devices in the smart grid, traditional cloud-edge-end architecture will face the increasing issue of response latency. In this context, in situ computing, as a paradigm for local or near-source data processing within cloud-edge-end architecture, is gradually becoming a key technological pathway in industrial systems. The in situ server system, by deploying servers near terminals, enables the near-data-source processing of terminal-generated in situ data, representing an important implementation of in situ computing. To enhance the processing efficiency and response capability of in situ data in smart grid scenarios, this study designs an in situ data processing mechanism and an access demand management framework. Due to the heterogeneity of in situ server performance, there are variations in response capabilities for access demands across rounds. This study introduces a double deep Q-network with prioritized experience replay to assist in response decision-making. Simulation experiments show that the proposed method reduces waiting latency and response latency by an average of 67.69% and 68.77%, respectively, compared to traditional algorithms and other reinforcement learning algorithms, verifying its effectiveness in in situ data management. This scheme can also be widely applied to in situ computing scenarios with low-latency data management, such as smart cities and industrial IoT.