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With the development of the computer network, assets evaluation has been widely used, which makes the network node trust issues become increasingly prominent. With reference to the sociology interpersonal trust model and based on the relationships and interpersonal entropy, this paper designed the credibility method under the dispersion assets evaluation environment, which reflects the dynamic nature of continuity and uncertainty clearly. The model obtains the asset appraisal reliability directly from the history mutual information, and revise the asset appraisal reliability according to the credit records with each node and credibility functions directly, and then consider the revised credibility information of each node comprehensively, using B constraint synthesis to calculate the comprehensive reliability assets appraisal, then make use of the simulation analysis of the new model to evaluate the effect of assets evaluation credibility and discuss the future work in the end. [ABSTRACT FROM AUTHOR]

The health status of the battery of new energy electric vehicles is related to the quality of vehicle use, so it is of high practical application value to predict the health status of the battery of electric vehicles. In order to predict the health status of lithium battery, this study proposes to optimize the empirical modal decomposition method and obtain the ensemble empirical modal decomposition algorithm, and use this algorithm to collect the vibration signal of the battery, then use wavelet transform to pre-process the collected signal, and finally combine K-mean clustering and particle swarm algorithm to cluster the signal types to complete the prediction of battery State of Health. The experimental results show that the ensemble empirical modal decomposition algorithm proposed in this study can effectively perform signal acquisition for different state types of batteries, and the K-mean clustering-particle swarm algorithm predicts a 63 % decrease in the health state of the battery at 600 cycles, with a prediction error of 2.6 %. Therefore, the algorithm proposed in this study is feasible in predicting the battery health state., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)