Internet-based cloud computing is currently an important core technology for computer development in China. It can be used not only in marketing but also in various industries. At the same time, people-oriented and green projects and products that focus on the development of the ecological environment and green consumption dominate the current market trend. The purpose of this paper is at studying the development model of enterprise green marketing based on cloud computing. This article compares and analyzes enterprise green marketing systems through big data algorithms and statistical methods. It starts with the basic characteristics of cloud computing, studies the opportunities and challenges that cloud computing brings to enterprises’ green marketing efforts, describes the green marketing processes and characteristics of enterprises, and analyzes cloud. The feasibility of the construction of the computing system, the basic architecture of the cloud computing system, and the construction of a complete cloud computing data processing flow are proposed. The research data found that the combination of the green marketing development model and cloud computing in the enterprise operating system is conducive to the development of the enterprise; it improves the discovery efficiency of the enterprise and reduces the pollution in the production of the enterprise; the cloud computing can greatly improve the work efficiency of the employees. Cloud computing can improve employees’ speed to complete tasks by about 20% and reduce the error rate by about 50%. The cloud computing enterprise green marketing development model has guiding significance for the long-term development of the enterprise.
With the development of China’s economic globalization, more and more enterprises have realized the importance of risk management. As a new technology, machine learning has injected new vitality into enterprise risk management. This paper analyzes the factors of enterprise risk from the perspective of risk management, explores and analyzes the application of machine learning in enterprise risk management, and points out the direction for scientific enterprise risk management. This paper first establishes a set of credit evaluation factors and then builds a model based on this evaluation factor set, which ensures the accuracy of the evaluation and also adds pertinence. With the increase of the number of samples, the accuracy of the prediction results also increases. Also, basically, with the increase of training samples and realistic samples, the accuracy rate continues to increase. In the data of the experimental results, it is shown in the process of increasing the number of samples from 400 to 600. The accuracy of the traditional method is 0.81 and 0.8316, respectively. The accuracy of the improved method is 0.842 and 0.905, respectively. This paper studies the process of enterprise risk assessment from the perspective of risk management and attempts to build an enterprise risk management model based on industry risk coefficients, which have certain practical significance.
In recent years, with the rapid development of the global economy and the development trend of more and more stable, well-developed network communications, online shopping has become an increasingly common way; as a result, the logistics industry has emerged from many industries and has become one of the most popular industries. However, due to the extensive involvement of the logistics industry, the overly complex technology, and the huge amount of data and information, the security of logistics has become one of the hot topics of special concern. Based on the background of an intelligent environment, this paper constructs a supply chain financial logistics supervision system based on Internet of Things technology. This article refers to the research experience of previous scholars, briefly introduces the theoretical knowledge of the Internet of Things technology, smart environment, and supply chain finance, and makes a certain analysis of the logistics supervision system. We collect and calculate logistics data through the wolf group hunting and siege formula in the wolf group algorithm and analyze the application performance of the logistics supervision system in reality. Then, we briefly designed the system architecture diagram of the logistics supervision system and compared the freight situation of the logistics supervision system before and after and statistics on the deployment of the logistics supervision system in customs, docks, airports, stations, and other places from 2015 to 2019. Finally, a comparative analysis of the performance of wolf pack algorithm and other algorithms was performed under different path planning. The final result shows that the logistics supervision system has important practical value in the logistics industry; in addition, the deployment of logistics supervision systems in customs, terminals, and other places has increased year by year from 2015 to 2019.
With the deepening of the global economic community, various emergencies emerge in endlessly, and the risks gradually increase. People's lives and property are threatened, which also causes a great burden on the social economy. Hitherto unknown novel coronavirus events occurred in China after the outbreak of the new coronavirus in 2019. The emergency management system is not perfect, so we start to study and improve the deficiencies of the emergency management system, but it is still difficult to effectively prevent and deal with all kinds of sudden and frequent social problems. Therefore, this paper puts forward the research of intelligent evaluation system of government emergency management based on BP neural network. In this paper, an intelligent evaluation system of government emergency management based on Internet of things environment is established, and then the system is deepened by BP neural network algorithm to avoid the interference of human factors. An objective intelligent evaluation system of government emergency management is constructed and verified by an example. We applied the system in a province, and proved that the system has strong executive ability, outstanding big data computing ability, and can objectively evaluate and analyze the government emergency management. The operability and accuracy of the intelligent evaluation system are verified. The effective evaluation content provides a new idea and method for government emergency management. And then continuously improve the emergency management measures to achieve the effect of dealing with things smoothly without panic.
With the continued economic downturn, coupled with the slowdown of domestic economic development and fierce market competition, the development of small and medium-sized enterprises has become more and more difficult. Because the financial background and operating strength of small and medium-sized enterprises are far inferior to large-scale enterprises, coupled with the influence of global economic integration, many small and medium-sized enterprises have gradually closed down. Therefore, exploring the entropy coupling algorithm is of great significance to the role of corporate leadership strategy management. This paper studies the coupling conditions and coupling process of corporate strategy and business model, builds a new coupling model, and goes deep into the coupling model to study the cooperation mechanism between its internal modules and initially builds the company to break through the two major dilemmas at the same time. This paper uses entropy theory to evaluate corporate leadership strategy, constructs an evaluation index system based on entropy, determines the weight of each index, and calculates the entropy value. This paper uses the alpha coefficient to test the reliability of the questionnaire. The value range of α coefficient is [0, 1], and different values represent different reliability. Large enterprises as a whole are mostly in the highly coupled (41.80%) and moderately coupled (27.34%) stages; medium-sized enterprises as a whole are mostly in the highly coupled (39.50%) and moderately coupled (31.50%) stages; small enterprises as a whole are mostly in the moderately coupled (39.50%) and moderately coupled (40.72%), low coupling (33.20%), and high coupling (25.9%) stages; microenterprises as a whole are mostly in the low coupling (43.70%), moderate coupling (36.41%), and high coupling (30.51%) stages. The results show that the entropy coupling algorithm can improve the deficiencies in the leadership strategy and provide a practical and reliable path for carrying out leadership development projects.
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