Metallurgy crown block operation early warning method and system based on artificial intelligence

A technology of artificial intelligence and cranes, applied in the direction of load suspension components, transportation and packaging, safety devices, etc., can solve the problems of not being able to obtain accurate dangerous area ranges, and not being able to issue dangerous warnings in time

Inactive Publication Date: 2021-04-30
李俊
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AI Technical Summary

Problems solved by technology

[0004] It is impossible to obtain the accurate range of the dangerous area durin

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  • Metallurgy crown block operation early warning method and system based on artificial intelligence
  • Metallurgy crown block operation early warning method and system based on artificial intelligence
  • Metallurgy crown block operation early warning method and system based on artificial intelligence

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Embodiment Construction

[0027] In order to further explain the technical means and effects adopted by the present invention to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, will describe an artificial intelligence-based metallurgical crane operation early warning method and system according to the present invention. Its specific implementation, structure, feature and effect thereof are described in detail as follows. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0028] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field of the invention.

[0029] The specific scheme of an artificial...

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Abstract

The invention relates to the technical field of artificial intelligence, in particular to a metallurgy crown block operation early warning method and system based on artificial intelligence. According to the method, the dangerous area range during operation of a crown block is judged by combining the swing range, the shaking range and the shaking angle of a cross beam lifting appliance and the swing range of an operation object; when the operation object is the liquid in a tank, the splashing dangerous area range of the liquid is obtained in combination with the splashing condition of the liquid, the splashing dangerous area range is combined with the dangerous area range obtained during operation of the crown block, and the final dangerous area range obtained during operation of the crown block is obtained; and the dangerous area range during crown block operation can be accurately judged in real time, so that the dangerous situation during crown block operation is judged in time, and early warning can be given to workers in the dangerous area in time.

Description

technical field [0001] The invention relates to the technical field of artificial intelligence, in particular to an artificial intelligence-based early warning method and system for metallurgical crane operations. Background technique [0002] During the converter steelmaking process, the crane is responsible for the lifting and lowering of all heavy objects. Different from traditional hoists, heavy objects such as molten iron tanks or ladles filled with molten iron will be lifted in the converter steelmaking scene. During the production process, the operation of the crane requires the cooperation of the driver and the ground commanders. Dangerous situations that often occur include: liquid splashing in the molten iron tank or ladle, and operators around the heavy lifting. When a dangerous situation occurs, the driver has no time to operate. [0003] In practice, the inventor finds that the above-mentioned prior art has the following defects: [0004] The accurate range o...

Claims

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Application Information

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IPC IPC(8): B66C13/16B66C13/46B66C15/06
CPCB66C13/16B66C13/46B66C15/065
Inventor 李俊陈艳
Owner 李俊
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