Multi-parameter online optimization algorithm of BP neural network algorithm in Internet of Things service

With the development of science and technology, the application of the Internet of Things (IOT) is becoming more and more widespread. Applying BP neural network algorithms (NNA) to the IOT system will help improve the performance of the IOT system. The research purpose of this paper is to solve the problems of long-parameter measurement cycle and untimely feedback of the existing IOT online measurement system. In this paper, a multi-parameter (MP) IOT online measurement system based on BP NNA is designed, and a simulation test experiment is performed. The MP online measurement IOT system based on the BP NNA completes the parameter collection, analysis, and display through the perception layer, network transmission layer, and application layer. The core is that the system application layer adds the BP NNA to optimize real-time acquisition parameters, processing to reduce parameter measurement time. It can be known from algorithm simulation experiments that the online measurement system based on the BP NNA proposed in this paper uses the BP NNA to predict the absolute error value of the final moisture content and the measured moisture content within 0.3, and the absolute error of the moisture content value in actual production. It is acceptable in the range of 0.5, which speeds up data collection time. This system has a very good effect on improving the feedback adjustment speed of the manufacturing process system.