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With the rapid advancement of the informatization process, enterprise informatization management has received more and more attention. Facing the increasingly complex and changeable social and economic environment, the difficulty of enterprise risk management has gradually increased. How to establish an efficient risk management mechanism for early warning of corporate risks is the goal that companies seek. Traditional statistical analysis can no longer satisfy the processing of massive financial data. Therefore, how to find useful information for the financial risk early warning management of the enterprise from the large amount of financial data information generated by the business activities of the enterprise is a problem that enterprises urgently need to solve at present. The continuous improvement and innovation of data mining technology and the good performance of research and analysis of massive data have made the two closely linked. First, this study introduces the theories of financial risk early warning and data mining technology; second, it introduces the research process of financial risk early warning model and elaborates the three data mining techniques used in this study; then combined with the actual situation of listed companies in my country, it constructs financial risk early warning index system; and finally, 77 listed manufacturing companies and their matching companies that were first processed by ST in 2005-2007 were used as research samples, based on the financial data of the 2.4 years before being processed by ST and CXISP. It is found that the financial risk early warning model established by data mining technology has strong early warning capabilities. From the perspective of the prediction capabilities of the three models, the closer the time to ST, the higher the accuracy of the prediction; from the perspective of short-term early warning, the three models have better prediction effects, but from the perspective of long-term early warning, the prediction effects of neural networks and decision trees are better than logistic regression of statistical analysis; data mining techniques based on knowledge discovery are not only suitable for short-term early warning but also for longer-term early warning. Therefore, data mining can be applied to financial risk early warning analysis to achieve the purpose of using data mining technology for decision support.

Neural network is used to deal with the nonlinear relationship, usually there is a strong nonlinear relationship between input and output. Through the self-learning of neural network, the weight of data samples is determined after training, and the optimal solution is obtained according to the process steps. In this paper, thea authors analyze the risk assessment of logistics finance enterprises based on BP neural network and fuzzy mathematical model. For logistics companies, it is necessary to determine the ability of logistics companies to engage in logistics finance business, and then to make detailed and accurate grasp of relevant information. The difference between the actual output and the expected output of the training sample is small, so the fitting is completed well, and the parameters of the neural network are further adjusted. The results show that the model has a good ability of learning nonlinear function relations. To sum up, in order to reduce logistics financial risks, we must fully understand the factors that affect logistics financial risks, determine the proportion of risk factors, and then use the fuzzy evaluation method to analyze the financial business risks.

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Although cloud computing application is rising, some unanswered problems still exist because of its inner issues like untrustworthy inactivity, the absence of movement backing, and place awareness. Therefore, fog computing has appeared as a hopeful infrastructure to supply flexible resources at the edge of the network. Fog supplies processing, data, storing, and application amenities to ultimate operators. The management strategies have a great impact on fog computing, such as monitoring and optimizing, the correlated components for improving the performance, availability, security, and any fundamental operational requirement. However, as far as we know, no organized study exists about analyzing their importance in fog environments. This paper provides a detailed survey for covering the current state‐of‐the‐art in fog management. This paper classifies the management strategies into three main categories: data management, energy, and resource. We also presented a few prospects and problems like the suggestions for the upcoming studies in the associated methods requiring to be investigated in fog computing. Moreover, a new paradigm has been ensured by increasing fog functionality and resource consumption. In general, fog management strategies in computing environments still need improvements in the variety of its setting to convert to an on‐request method, decrease the associated overhead, and improve the performance. Therefore, proper management can maintain the least resource consumption, which will finish the extra decrease in energy usage. Therefore, we contribute to provide strong suggestions for future fog computing studies. This paper can handle the pace of publications and propose the outcomes of study and practice as an upcoming route for decision makers in healthcare. In principle, the increase of researchers, scientists and managers' awareness level would increase the managers' good and knowingly behavioral conduct on managing fog environments.