In the process of intelligent system operation fault diagnosis and decision making, the multi-source, heterogeneous, complex, and fuzzy characteristics of information make the conflict, uncertainty, and validity problems appear in the process of information fusion, which has not been solved. In this study, we analyze the credibility and variation of conflict among evidence from the perspective of conflict credibility weight and propose an improved model of multi-source information fusion based on Dempster-Shafer theory (DST). From the perspectives of the weighting strategy and Euclidean distance strategy, we process the basic probability assignment (BPA) of evidence and assign the credible weight of conflict between evidence to achieve the extraction of credible conflicts and the adoption of credible conflicts in the process of evidence fusion. The improved algorithm weakens the problem of uncertainty and ambiguity caused by conflicts in the information fusion process, and reduces the impact of information complexity on analysis results. And it carries a practical application out with the fault diagnosis of wind turbine system to analyze the operation status of wind turbines in a wind farm to verify the effectiveness of the proposed algorithm. The result shows that under the conditions of improved distance metric evidence discrepancy and credible conflict quantification, the algorithm better shows the conflict and correlation among the evidence. It improves the accuracy of system operation reliability analysis, improves the utilization rate of wind energy resources, and has practical implication value.

The low-carbon economy and sustainable development have become a widespread consensus. Chain supermarkets should pay attention to path optimization in the process of distribution to reduce carbon emissions. This study takes chain supermarkets as the research object, focusing on the optimization of the vehicle routing problem (VRP) in supermarket store distribution. Firstly, based on the concept of cost-effectiveness, we constructed a green and low-carbon distribution route optimization model with the lowest cost. With cost minimization as the objective function, the total distribution cost in the vehicle delivery process includes fixed cost, transportation cost, and carbon emission cost. The carbon emission cost is calculated using the carbon tax mechanism. Secondly, through integrating the Floyd algorithm, the nearest neighbor algorithm, and the insertion algorithm, a fusion heuristic algorithm was proposed for model solving, and an empirical study was conducted using the W chain supermarket in Wuhan as an example. The experimental results show that optimizing distribution routes considering carbon emission cost can effectively reduce carbon emissions. At the same time, it can also reduce the total costs of enterprises and society, thereby achieving greater social benefits at lower costs. The research results provide effective suggestions for chain supermarkets to control carbon emissions during the distribution process.

This work proposes a new literature topic clustering analysis framework, based on which the topics of digital-economy-related studies are condensed. First, we calculated the word vector of keywords using the FastText model, and then the keywords were merged according to semantic similarity. A hierarchical clustering method based on the Jaccard coefficient was employed to cluster the domain documents. Finally, the information gain method was applied to estimate the high-gain feature words for each category of topics. Based on the above framework, 23 categories of research topics were formed. We divided these topics into layers of digital technology, convergence innovation and digital governance, and we constructed a three-level digital economy research framework. Thereafter, the current hot spots and frontier trends were derived based on the number and growth rate of the literature. Our study revealed that the research on digital technology, which is the basic layer of the digital economy, has waned. The field related to the integration and innovation of digital technology and the real economy was the current research focus, among which the results with respect to “New Business Forms in the Digital Age”, “Circular Economy” and “Gig Economy” were abundant. The problems of the unbalanced development of the digital economy and digital monopoly have strengthened research on digital governance. Furthermore, research on “Regional Digital Economy”, “Chinese Digital Economy” and “Data Management” is in its initial stage and is a potential area of future research.

The use of renewable energy sources, such as wind power, has received more attention in China, and wind turbine system reliability has become more important. Based on existing research, this study proposes a state reliability prediction model for wind turbine systems based on XGBoost–LSTM. By considering the dynamic variability of the weight fused by the algorithm, under the irregular fluctuation of the same parameter with time in nonlinear systems, it reduces the algorithm defects in the prediction process. The improved algorithm is validated by arithmetic examples, and the results show that the root mean square error value (hereinafter abbreviated as RMSE) and the mean absolute error value (hereinafter abbreviated as MAPE) of the improved XGBoost–LSTM algorithm are decreased compared with those for the LSTM and XGBoost algorithms, among which the RMSE is reduced by 8.26% and 4.15% and the MAPE is reduced by 24.56% and 27.99%, respectively; its goodness-of-fit R2 value is closer to 1. This indicates that the algorithm proposed in this paper reduces the existing defects present in some current algorithms, and the prediction accuracy is effectively improved, which is of great value in improving the reliability of the system.

Under uncertain environmental conditions, a nonlinear system may encounter problems like information conflicts, ambiguity, loss, and unclear interdependencies, which can result in low accuracy in predicting abnormal system states. System failures lead to negative impacts. To address these issues, this study proposes an analysis model that incorporates factor weight adaptive adjustment and integrates Dempster-Shafer’s theory (D-S theory) algorithms to construct an evaluation model, and to quantify uncertainty factors and correlation factors within the system. This approach reduces the impact of uncertainty in information features on analysis accuracy, enhances the precision of system reliability state probability assessment, and improves decision-making management levels. The results of the wind turbine system case study indicate that the proposed algorithm achieves a system state identification accuracy of 97% and a system reliability probability of 65%, with an overall accuracy improvement of 4.5% compared to traditional algorithms and a reliability probability assessment accuracy improvement of 5.22%, better aligning with the actual system’s state probability distribution.