The era of big data is officially coming, marking that my country has officially entered the era of massive information. Computer information processing technology is an innovative product of the rapid development of my country’s scientific and technological information field, which is of great significance to the development of society. In this regard, this article mainly discusses the application of computer information processing technology in the context of big data, hoping to bring theoretical help and guidance to relevant technical personnel.
The stability and accuracy of temperature control of the injection molding machine is one of the keys to determine the quality and appearance of plastic parts. Due to the temperature control system of water-cooled injection molding machine has the characteristics of coupling, nonlinearity, and hysteresis, the traditional proportion-integral-derivative (PID) method to control the barrel temperature of injection molding machine will yield temperature overshoot and oscillation, affecting the product and appearance quality. In order to improve the effect of barrel temperature control, in this paper, an expert adjustable fuzzy control strategy (EAFCA) is designed to optimize the barrel temperature control system through the combination of expert control, fuzzy control, and PID control. The expert control rules are designed according to the barrel temperature deviation e and its change rate ec. The expert rules are used to adjust the mapping range of the fuzzy universe in real time for the input and output variables of the fuzzy controller and finally to realize the accurate adjustment of the PID controller parameters. In terms of control systems, the distributed control system (DCS) monitoring system of injection molding machine is designed with Siemens CPU315-2PN/DP and industrial computer as the hardware core and Step7 and windows control center (WinCC) as the software platform to complete the tool plastic production monitoring system. The DCS improves the operability, data management convenience, and plastic production efficiency of the injection molding machine monitoring system. The Simulink simulation and field test show that the EAFCA method can increase the adjustment speed by approximately 34 s and reduce the overshoot by nearly 3.1%, which significantly improves the stability and accuracy of the barrel temperature and improves the quality of plastic parts.
The data characterization is effectively explored based on the human cognitive features of data visualization conforming to the characteristics and habits of human beings. It analyzes the principle and characteristics of human cognition, and interprets the advantage of the graphic symbols and visual thinking in cognitive process. Furthermore, a model of the big data visualization is proposed based on the human cognitive law, which includes four main parts: strategic level, scope layer, structural layer and data presentation layer. It gives an example to expatiate that the current visualization of the cognitive model can be applied to the related data visualization, and realize the mapping between the visual cognitive thinking and data visualization.
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