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.

中文翻译：

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大数据下资源受限型工业项目组调度的网络流量统计方法

随着互联网时代的到来，各领域对网络的需求不断增长，网络应用日益丰富，给网络流量统计带来了新的挑战。如何高效、准确地进行网络流量统计成为研究的重点。目前的研究成果虽然很多，但都不是很理想。本文基于大数据和机器学习算法的时代背景，利用蚁群算法解决典型的资源受限项目调度问题，寻找网络流量资源分配问题的最优解。首先建立资源受限项目调度问题的目标函数和数学模型，并利用蚁群算法进行优化。然后，引入PSPLIB中的项目调度问题，包含10个任务和1个可再生资源。利用数学模型和蚁群算法解决资源受限的项目调度问题。最后对忙时IP为112.58.14.66的PCU的数据量和频率进行分析统计。实验结果表明，该算法能够得到第94代后的唯一最优解，说明该求解方法中设置的参数是合适的，可以得到最优解。最优调度方案中每个任务的调度非常紧凑合理。网络流量的高峰时间通常在9:00到19:00-21:00之间，我们可以根据这些时间段合理调度网络资源。所以，