{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T14:13:10Z","timestamp":1753884790152,"version":"3.41.2"},"reference-count":35,"publisher":"World Scientific Pub Co Pte Ltd","issue":"04","funder":[{"name":"Intelligent agriculture trusted traceability system based on blockchain","award":["20220202036NC"],"award-info":[{"award-number":["20220202036NC"]}]},{"name":"Research on intelligent monitoringand early warning system of rice diseases and pests based on meteorological conditions","award":["20210203211SF"],"award-info":[{"award-number":["20210203211SF"]}]},{"name":"Research on key technology of maize kernel selection based on convolution neural network","award":["JJKH20210335KJ"],"award-info":[{"award-number":["JJKH20210335KJ"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J CIRCUIT SYST COMP"],"published-print":{"date-parts":[[2024,3,15]]},"abstract":"<jats:p> Aiming at the problem of long task calculation delay of traditional edge computing offloading strategy (COS), a joint caching and COS for power supply chain services under cloud edge collaborative computing environment is proposed by introducing the master-slave game model. First, for the network of information interaction between power grid system and electrical equipment suppliers, the corresponding overall model of cloud edge collaborative network is constructed by combining cloud computing, edge computing, supplier terminal communication and remote cloud. Then, the cloud-side collaborative network model is analyzed and solved by building the service function cache model, task unloading and data transmission model and power business data calculation model, respectively. Finally, by introducing the master-slave game model, the whole system is divided into three different interests of power service providers, communication providers and communication network terminal providers and the joint caching and COS for power supply chain services is given. Through simulation experiments, the proposed computing unloading strategy and the other three methods are compared and analyzed under the same experimental conditions. The results show that the service cache hit rate of the proposed method is the highest, reaching 96.35%. Compared with the other three comparison methods, it increased by 14.96%, 11.24% and 4.85%, respectively. In addition, its task computing delay is the least affected by agent computing power and agent computing resources, and its performance is better than the other three comparison methods. <\/jats:p>","DOI":"10.1142\/s0218126624500671","type":"journal-article","created":{"date-parts":[[2023,11,23]],"date-time":"2023-11-23T09:28:34Z","timestamp":1700731714000},"source":"Crossref","is-referenced-by-count":1,"title":["Joint Caching and Computing Offloading Strategies for Power Supply Chain Services Under Cloud Edge Collaborative Computing Environment"],"prefix":"10.1142","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-9754-9851","authenticated-orcid":false,"given":"Shi","family":"Yang","sequence":"first","affiliation":[{"name":"School of Information Engineering, Changchun University of Finance and Economics, Changchun, Jilin 130122, P. R. 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