Wireless temperature remote monitoring system based on Wi-Fi

Abstract

The invention relates to a wireless monitoring system based on improved BP neural network PID controller. According to the work flow of the system, an industrial personal computer accesses an MMF module through a wireless network which comprises a Wi-Fi module, and monitors the work state of a resistance furnace temperature device in real time; and the MMF module adjusts and controls output according to an improved BP neural network PID control algorithm, so that temperature change quickly reaches a steady state on the premise of small overshoot. The system provided by the invention is composed of a hardware system and software implanted into the MMF module. The hardware system is composed of a management unit, a wireless transmission unit and a control unit. According to the invention, a Wi-Fi network is used as a transmission medium; the system has the advantages of simple stationing and short network-laying construction period; and the destructed network can be easily recovered.

Description

technical field [0001] The invention relates to a Wi-Fi-based wireless temperature remote monitoring system, which belongs to the technical field of temperature process automatic control. Background technique [0002] In recent years, my country's industrialization driven by informatization is booming, and temperature has become an important parameter in the control of industrial objects. Especially in metallurgy, chemical industry, machinery and other industries, various heating furnaces, heat treatment furnaces, reaction Furnace etc. Due to the different types and principles of stoves, the heating methods and fuels used are also different, such as coal gas, natural gas, oil and electricity, etc. For temperature control under different production conditions and process requirements, the heating methods used, the fuels selected, and the control schemes are also different. With the continuous development of industrial technology, traditional control methods can no longer mee...

AI Technical Summary

Problems solved by technology

Using the traditional PID algorithm to control the temperature process, the steady-state response characteristics are better, but it is difficult to obtain satisfactory dynamic response characteristics

The PID algorithm based on BP neural network can adjust parameters online to improve the dynamic performance of the system, but the BP neural network has the defect of slow convergence speed, and may fall into a local minimum point during the convergence process, and cannot complete the optimal tuning of parameters

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