Epidemic risk has great uncertainty and harmfulness, which poses a potential threat to public health in a certain region. Establishing a special risk assessment system to assess and predict the potential epidemic risk of a region can effectively avoid or reduce the impact of epidemic risk. Therefore, this paper combs the related factors that affect the epidemic risk, and proposes an epidemic risk assessment model based on 12 indicators by combining Markov chain and AHP. The model can assess the epidemic situation in a certain region from four aspects: the probability of risk occurrence, the probability of loss, the possibility of risk disappearance and risk duration, so as to provide detailed data for the risk management and control of epidemic in the region, and help the epidemic prevention work to be carried out in a targeted way. Finally, the case analysis and method comparison are carried out,and the results show that the model proposed in this paper is reasonable and feasible.

The massive cloud service market is full of various services with uneven quality. Even the services that have passed the platform detection will have unknown trustworthiness problems in the actual use process. The risk environment of the cloud service determines that its trustworthiness is random. The static trustworthiness assessment results can only reflect the cloud service trustworthiness at a certain time, not enough to reflect the real trustworthiness of the cloud service. To objectively reflect the trustworthiness of the cloud service, it is necessary to further assess the cloud service trustworthiness state and its changes on the basis of trustworthiness level measurement. To solve this problem, this paper combs the trustworthiness indicators of the cloud service, puts forward an effective assessment method of cloud service trustworthiness level based on D-S theory, and puts forward the representation method of cloud service trustworthiness state and its transition state combined with Markov chain, so as to realize the effective assessment of cloud service trustworthiness state and its changes. Finally, through case analysis, it shows that the method proposed in this paper is feasible, can effectively assess the cloud service trustworthiness state and its changes, and provide users with detailed assessment results, so as to help users make reasonable service selection and trustworthiness management. This research has important significance for ensuring the cloud service trustworthiness and improving the cloud service market security.

The popularity of cloud computing has made cloud services gradually become the leading computing model nowadays. The trustworthiness of cloud services depends mainly on construction processes. The trustworthiness measurement of cloud service construction processes (CSCPs) is crucial for cloud service developers. It can help to find out the causes of failures and to improve the development process, thereby ensuring the quality of cloud service. Herein, firstly, a trustworthiness hierarchy model of CSCP was proposed, and the influential factors of the processes were identified following the international standard ISO/IEC 12207 of the software development process.Further, a method was developed combined with the theory of information entropy and the concept of trustworthiness. It aimed to calculate the risk uncertainty and risk loss expectation affecting trustworthiness. Also, the trustworthiness of cloud service and its main construction processes were calculated. Finally, the feasibility of the measurement method were verified through a case study, and through comparing with AHP and CMM/CMMI methods, the advantages of this method were embodied.

Infectious diseases risk is directly related to human life safety. After the COVID-19 pandemic, people have paid unprecedented attention to the risk of infectious diseases. Compared with treatment after the outbreak of the epidemic, identifying the influencing factors of infectious disease risk and quantitatively analyzing and assessing infectious disease risk before the outbreak of the epidemic plays an equally important role. This article focuses on the risk of irregular outbreaks of infectious diseases. On the one hand, a method based on information gain is proposed to calculate the weight of environmental factors directly related to infectious disease risk, to clarify the correlation between environmental factors and infectious disease risk. On the other hand, the risk calculation method based on risk weight number is proposed to calculate the risk level of different infectious diseases under the influence of specific environmental factors. Finally, the effectiveness and feasibility of the proposed method are verified through case analysis and discussion. By comparing it with other risk assessment methods, the advantages and disadvantages of the proposed method are demonstrated.

Edge computing is playing an increasingly important role in the field of health care. Edge computing provides high-quality personalized services to patients based on user and device data information. However, edge nodes will collect a large amount of sensitive patient information, and patients will also bear the risk of privacy disclosure while enjoying personalized services. How to reduce the risk of privacy disclosure while ensuring that patients enjoy personalized services brought by edge computing is the research content of this paper. In this paper, the work flow and management mode of Hospital Information System (HIS) are investigated on the spot, and the risk-adaptive access control model based on entropy is established. First, we use International Classification of Diseases, Tenth Revision (ICD-10) to mark the information resources accessed by users and use information entropy to measure the correlation α between medical information accessed by users and work tasks. Finally, we analyze the relationship between correlation α and risk through an example. The results show that users with high correlation α have low risk of access behavior, and users with low risk have high correlation α of access information resources and work goals. This discovery can help managers predict users access behavior in the Big Data environment, so as to dynamically formulate access control policies according to the actual access situation of users and then realize the privacy protection of medical big health data.

China is currently undergoing a transition towards high-quality economic development, and the industrial structure is being optimized. However, there are still regional imbalances in economic development and energy consumption. Therefore, it is necessary to investigate whether the disparities in electricity intensity between provinces and industries can converge over time. This paper investigates the trend towards low-energy consumption in China and verifies the club convergence of electricity intensity across provinces and industries in China using a data-driven log t-test and club clustering algorithm. The study innovatively finds that the convergence of electricity intensity is more pronounced in the tertiary sector. The value added of electricity consumption in the tertiary sector is closely related to the level of regional economic development, further indicating that the pattern of low-energy development is closely related to factors such as the geographical location of the province, regional radiation capacity, and industrial structure. In order to accelerate low-energy development in China, the paper proposes policy recommendations related to low-energy development to improve the quality of China’s economic development.