Energy Efficiency Calculation And Lightweight Transmission Strategy For Power Users Based On Edge Nodes

The energy market with electric energy as the core is developing steadily towards green low-carbon and collaborative diversification.The new round of energy revolution,which deeply integrates emerging technologies such as " cloud big things and mobile intelligence " and new energy technologies,is boosting the high-quality realization of the " double carbon " vision.As an important organic unit in the power market system,power users have a large number of energy-using equipment.It is of great practical significance and engineering value to carry out intelligent data service with energy efficiency data analysis and processing as the core.In this context,in order to further meet the needs of users for timely feedback and adjustment of electricity energy efficiency,this paper focuses on the key issues of data streamlining storage,energy efficiency evaluation and compression uploading in the current energy efficiency data analysis and processing of power users under the edge computing mode.The specific work is mainly reflected in the following three aspects:(1)Based on the Pressure-State-Response logical framework,the energy efficiency evaluation index system of power users is constructed,and the edge-side simplified energy efficiency index is determined.Firstly,the Pressure-State-Response conceptual model of user energy efficiency is systematically analyzed.On this basis,according to the causal logic relationship between indicators,the evaluation indicators are selected from multiple dimensions and the energy efficiency index system is constructed.Secondly,considering the limited storage resources of edge nodes,the importance,balance and independence of the indicators are abstracted from the index system to quantify the three indicators.The quantitative values of the three attributes are fused by the influence degree and the optimization degree model,and the cooperative game theory is used to optimize the index ranking.Finally,the edge side is determined to simplify the index set,so as to effectively remove the redundancy of the edge side data.(2)Based on the CRITIC weighting method,the energy efficiency index weight is determined,and an improved grey TOPSIS energy efficiency evaluation model for power users is proposed.Firstly,the CRITIC weight calculation method is used to make full use of the data information of the indicators,and a more objective weight coefficient is given to the proposed energy efficiency indicators.Secondly,the absolute ideal solution is constructed by improving the grey TOPSIS evaluation model to effectively avoid the inverse ranking problem caused by the dynamic change of the number of users.The introduced grey correlation degree can make up for the defect that the European criterion cannot accurately measure the advantages and disadvantages of users in the traditional method.Finally,by comparing with the typical methods,the effectiveness and accuracy of the improved grey TOPSIS evaluation method based on CRITIC weighting is verified.(3)In order to solve the problem of high bandwidth and long delay when data is uploaded to the cloud,a data lossless compression method DLZH based on differential coding is proposed by analyzing the characteristics of energy efficient data transmission.Firstly,based on the idea of entropy reduction transform,the energy efficiency data is losslessly compressed by differential coding to improve the compressibility of the data.Secondly,LZW coding is combined with Huffman coding to solve the problem of pattern redundancy and coding redundancy in data redundancy.The experimental analysis shows that the DLZH compression algorithm has good performance in compression rate,memory usage and transmission time.In order to verify the application effect of the method proposed in this paper under the edge computing architecture,the experimental environment is built and the experimental scheme is designed by using the basic platform of the laboratory.Through the experimental comparison of the edge computing framework scheme and the cloud computing framework scheme,the research advantages of energy efficiency data analysis and processing at the edge nodes are proved,which provides a certain reference for the related research in the field of power energy.