Access to public health services is a cause that benefits the people and concerns the vital interests of the people. Everyone has access to basic health care services. The continuous improvement in people’s health is an important indicator of the improvement in people’s quality of life. This paper selects data from the European Union (EU) on aspects of public health expenditure, medical care resources, and government emergency coordination capacity from the period 2008 to 2017. Principal component analysis and factor analysis are used to measure their public health service capacity scores and conduct a comparative analysis. On this basis, the TOBIT model is adopted to explore the driving factors that lead to the spatial differentiation of public health service capabilities, and to combine it with the data of the COVID-19 epidemic as of 8 August 2020 from the official announcements of the World Health Organization and governments for further thinking. The results indicate that the public health service capacity of countries in the EU is showing a gradual increase. The capacity in Western Europe is, in turn, higher than that of Northern Europe, Southern Europe and Eastern Europe. In addition, the overall capacity in Western Europe is relatively high, but it is not balanced and stable, while Northern Europe has remained stable and balanced at a high level. Population density, degree of opening up, education level, economic development level, technological innovation level, and degree of aging have a positive effect on public health service capabilities. The level of urbanization has a negative effect on it. However, in countries with strong public health service capabilities, the epidemic of COVID-19 is more severe. The emergence of this paradox may be related to the detection capabilities of countries, the high probability of spreading thCOVID-19 epidemic, the inefficient implementation of government policy, the integrated system of the EU and the adverse selection of youth. This paper aims to improve the ability of the EU to respond to public health emergencies, improve the utilization of medical and health resources, and better protect people’s health from the perspective of public health service capacity.

With the advent of the Internet era, the demand for network in various fields is growing, and network applications are increasingly rich, which brings new challenges to network traffic statistics. How to carry out network traffic statistics efficiently and accurately has become the focus of research. Although the current research results are many, they are not very ideal. Based on the era background of big data and machine learning algorithm, this paper uses the ant colony algorithm to solve the typical resource‐constrained project scheduling problem and finds the optimal solution of network traffic resource allocation problem. Firstly, the objective function and mathematical model of the resource‐constrained project scheduling problem are established, and the ant colony algorithm is used for optimization. Then, the project scheduling problem in PSPLIB is introduced, which contains 10 tasks and 1 renewable resource. The mathematical model and ant colony algorithm are used to solve the resource‐constrained project scheduling problem. Finally, the data quantity and frequency of a PCU with a busy hour IP of 112.58.14.66 are analyzed and counted. The experimental results show that the algorithm can get the unique optimal solution after the 94th generation, which shows that the parameters set in the solution method are appropriate and the optimal solution can be obtained. The schedule of each task in the optimal scheduling scheme is very compact and reasonable. The peak time of network traffic is usually between 9 : 00 and 19 : 00‐21 : 00. We can reasonably schedule the network resources according to these time periods. Therefore, the network traffic statistics method based on the solution of resource constrained industrial project group scheduling problem under big data can effectively carry out network traffic statistics and trend analysis.

I. Preventing and resolving systemic financial risks constitutes an enduring theme for safeguarding national financial security and maintaining steady and sustainable economic growth. The structura...

Operational command is the process of transforming the combat potential of the general into the actual combat effectiveness. Its impact on the operational process and outcome is not realized by directly destroying the operational targets, but by effectively controlling and dominating the troops. The effectiveness of such control and dominating is the operational command effectiveness. Strengthening the evaluation of operational command effectiveness can accurately grasp the command and control ability of commanders and command organs to their subordinate units during the training process, and can provide important support for improving the combat ability of troops in wartime. On the basis of mastering the basic situation of the synthetic brigade combat command, the synthetic brigade combat command effectiveness evaluation model is constructed by constructing the index system, studying the weight acquisition, and mastering the aggregation evaluation method. Finally, the feasibility of the command effectiveness evaluation method is confirmed through model verification.