Smart cloud clean room control method and system

Abstract

The invention discloses a smart cloud clean room control method and system. The method comprises the following steps that a communication system is initialized, and a data acquisition module acquiresenvironmental data in a clean room; and a central processing module independently divides each type of working environment data segment corresponding to the clean room into n equal parts, records theoffset value corresponding to each equal part point to form a data set and pre-stores the data set, and acquires the offset data set of the acquisition node corresponding to different environmental data acquisition nodes according to the pre-stored offset data set of the acquisition node. According to the invention, the environmental data acquisition node power with extremely small error can be generated by outputting the corresponding bias current, and the control deviation can be quickly and stably reduced.

Description

technical field [0001] The invention belongs to the technical field of clean rooms, and in particular relates to a smart cloud clean room control method and system. Background technique [0002] The clean room has high requirements on cleanliness. The main function is to control the cleanliness, temperature, humidity and pressure difference of the indoor atmosphere to minimize pollution. This requires real-time monitoring of the clean room environment. [0003] In the existing technology, the environmental monitoring system uses sensors for collection. Due to the large amount of electromagnetic equipment in the clean room, the collection system often has poor real-time transmission and large dynamic control deviations, which leads to a long data collection cycle and prone to various data collection nodes. The data oscillates, and the acquisition node cannot adapt to the complex environment and is prone to failure. Some minor faults are not easy to identify in the existing au...

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Problems solved by technology

[0003] In the existing technology, the environmental monitoring system uses sensors for collection. Due to the large amount of electromagnetic equipment in the clean room, the collection system often has poor real-time transmission and large dynamic control deviations, which leads to a long data collection cycle and prone to various data collection nodes. The data oscillates, and the acquisition nodes cannot adapt to complex environments and are prone to failures. Some minor faults are not easy to identify in the existing automatic image recognition. The combination of the two leads to inaccurate real-time monitoring of the clean room environment

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