With the rapid development of mobile Internet, operators are facing more and more fierce competition, traffic management is imperative. Accurate marketing based on user behavior analysis is an important means. However, in the era of big data, with the rapid growth of mobile Internet services and the increase in the number of users, the traditional architecture is difficult to adapt to the needs of mass data mining. The purpose of this paper is to make the Internet operators in China face a new development opportunity, start to move towards the road of traffic management to traffic management, and conduct an in-depth analysis of users’ behavior rules, to explore the real needs of the market and the majority of users. This paper proposes a cloud-based mobile Internet big data user behavior analysis engine solution, including the design of the overall system architecture, big data warehousing and preprocessing components, big data user behavior analysis model and other key modules, and finally analyzes the system test results.

The design and application of the equipment fault diagnosis system have been improved and upgraded, allowing it to effectively detect the equipment’s operation status and promptly eliminate hidden faults, reducing the occurrence of unexpected accidents and improving the safety index of people’s lives. The purpose of this essay is to design and apply neural network (NN) fault diagnosis system model in power Internet of things (IOT) equipment and explore its accuracy and effectiveness. The BP neural network (BPNN) algorithm was used to construct model of a fault monitoring testing of the power IOT equipment. Neural network is an algorithmic mathematical model that imitates the behavioral characteristics of animal neural network and performs distributed parallel information processing. The network parameters were as follows: there were four input layer nodes, seven hidden layer nodes, and five output layer nodes, the training times were 10000, and the allowable error was 0.002. In this paper, we use the IOT to detect model of a fault monitoring testing of power equipment designed in each sample, the success rate is as high as 97.5%, and the designed network structure and network parameters are reasonable. The trained loss is less than 0.001, and the nontraining set samples may be appropriately identified. It is clear that the NN has a high application for power equipment fault diagnosis in the IOT value.