Method for self-adapting to instrument state thresholds of different large-scale equipment

Abstract

The invention discloses a method for self-adapting to state thresholds of different large equipment instruments. At present, most of state statistics of large instruments and equipment of colleges andcompanies are mainly based on manual handwriting record statistics, and service life management of the equipment and reporting of faults cannot be effectively managed in time. According to the invention, the state evaluation of the large-scale instrument and equipment mainly uses a current information acquisition and power consumption use system to realize acquisition and processing of equipmentuse data, and through data comparison and statistical analysis, the operation condition of the large-scale instrument and equipment is diagnosed and analyzed mainly based on the technical means of theKmeans clustering algorithm. Whether the large instrument is in a normal operation state or not to realize an auxiliary decision function is judged. According to the invention, the equipment state can be adaptively judged, and the identification of three operation states is realized, therefore, the working state of the equipment at any time can be mastered in real time, and simplified, transparent and efficient management of the equipment is realized.

Description

technical field [0001] The invention relates to the field of acquiring state thresholds of large-scale equipment, in particular to a method for self-adapting the state thresholds of different large-scale instruments. Background technique [0002] With the continuous advancement of intelligent manufacturing, automation technology, digital information technology, and Internet of Things technology are booming, and more and more equipment replaces manual labor into actual production. The production efficiency of enterprises often depends on the utilization rate of production equipment. However, at present, the management of equipment in enterprises is still in a relatively lagging stage, and the management method of equipment is relatively simple and inefficient; the same is true for some enterprises or universities that practice scientific research, and scientific research and exploration often require expensive instruments Equipment support is particularly important for effect...

AI Technical Summary

Problems solved by technology

[0003] First, it is common to check the equipment one by one through manual on-site records. When there are too many production equipment, the management cost will rise sharply.

This method can only simply record the status of the equipment, but cannot check the working efficiency of the equipment, which makes it impossible for managers to allocate production resources reasonably and make effective use of them.

[0004] Second, a large number of instruments and equipment are left idle. With the development of time, such instruments will be eliminated by new instruments. This not only leads to the waste of national resources, but also affects the development of other institutions that need to conduct scientific research

[0005] Third, there are many types of equipment, and the working thresholds of various equipment are different. It is difficult to distinguish and obtain the current thresholds of different states such as shutdown, standby, and operation of the equipment.

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