As environmental protection changes from a purely social benefit to a social benefit mixed with an economic gain, the marketing strategy practice of corporate greenwashing is starting to become a popular research topic. However, the total number of existing review articles on greenwashing research is relatively small, and there are deficiencies in quantitative research and generalizability, scientificity and systematization. This paper attempts to bridge these gaps by combining systems theory with bibliometrics and using visualization techniques to analyze the evolution of knowledge structures and hotspots in greenwashing. Based on this, this paper takes the literature on greenwashing in the Web of Science core set database from 2004–2022 as a research sample and uses CiteSpace 5.8.R3 to conduct a descriptive statistical analysis and carve a visual map to reveal the evolution pattern of research in the field of greenwashing in a comprehensive and systematic way. The findings show that greenwashing research is prevalent in developed countries and continues to attract more scholars globally, focusing on the interpretation of greenwashing definitions and concepts, influencing factors, consequences and governance models. Finally, this paper introduces the system governance theory and improves organizational isomorphism theory to further generalize the existing research framework on greenwashing and provides an outlook on the future direction of greenwashing research from three perspectives: greening screening research, impact research and governance research.
With the advancement of smart IoT system, equipment modeling technology that adapts to the power Internet of Things becomes more and more important. In the power grid system, various types of equipment access processes are not standardized, the collected data format is not uniform, and the data is difficult to share. There is an urgent need to build a set of equipment modeling methods suitable for the power Internet of Things. By comparing traditional equipment modeling methods, we propose the static modeling structure of equipment in the power Internet of Things, and constructs a description method of terminal equipments on the cloud platform; combined with the construction requirements of the smart IoT system, design the Cloud-side collaborative dynamic modeling technology which based on equipment online status management, resource status management, alarm management and application management. At the same time, integrated equipment management methods for static modeling and dynamic modeling are realized in combination with application cases, providing technologies basis for unified device access, unified data transmission, and open sharing of data.
Wind power generation is currently one of the most promising power generation technologies. It is particularly important to improve the prediction accuracy of wind power output, which can effectively reduce the impact on the grid when wind power is connected to the grid. Based on the fractal model, this paper integrates it with the wind power prediction model, and combines the custom K nearest neighbor algorithm to evaluate the prediction effect using multi-dimensional indicators. Finally, taking the data of a wind farm in northwest china as an example, compared it with the prediction model of random forest, support vector machine and gradient boosting decision tree prediction model to verify the effectiveness of the prediction algorithm in this paper.
Dynamic load balancing is an important way to improve the resource utilization and parallel computing performance of multi-server system. For the mass of terminals under the Internet of Things scenario high frequency periodic escalation and receiving messages scene, the terminal equipment and message server need to establish a TCP long connection to communicate, the accumulation of messages different degree of weight will cause the load tilt. In this paper, an algorithm based on the socket buffer feedback mechanism is proposed, which takes the server load (CPU, memory, connection rate and socket buffer cache) as the load indicator, collects the load indicator through the load agent and calculates the discrete coefficient of the server socket buffer. The dispatch controller calculates the weight of each server, and the higher the weight, the lighter the server load. The experiments show that the CMTS more realistically feedbacks the server load status than the least connections. As the number of cluster server connections and message sending rate increase, the average cluster response time is shorter.
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