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A safety warning method for experimental environment based on multi-dimensional convolutional neural network

A convolutional neural network, security early warning technology, applied in biological neural network models, neural architectures, alarms, etc., to achieve high abstract information extraction capabilities, improve accuracy, and accurate conclusions.

Active Publication Date: 2021-07-27
哈尔滨工业大学人工智能研究院有限公司
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Problems solved by technology

[0004] The object of the present invention is to provide a kind of experimental environment safety early warning method based on multi-dimensional convolutional neural network, thus solve the aforementioned problems existing in the prior art

Method used

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  • A safety warning method for experimental environment based on multi-dimensional convolutional neural network
  • A safety warning method for experimental environment based on multi-dimensional convolutional neural network
  • A safety warning method for experimental environment based on multi-dimensional convolutional neural network

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Embodiment

[0066] In this embodiment, 80% of the normalized sample set is used as a training set, and 20% is used as a test set. Utilize the method proposed by the present invention and the designed artificial intelligence algorithm model to carry out safety early warning for the abnormality of laboratory monitoring data, judge whether there is a potential safety hazard and output the abnormal situation of the laboratory; the success rate of laboratory safety early warning can reach 90% %above.

[0067] By adopting the above-mentioned technical scheme disclosed by the present invention, the following beneficial effects are obtained:

[0068] The invention discloses an experimental environment safety early warning method based on a multidimensional convolutional neural network, which establishes a laboratory safety intelligent early warning model based on collected laboratory data, and conducts deep mining on the laboratory data through a multidimensional convolutional neural network, the...

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Abstract

An experimental environment safety early warning method based on multi-dimensional convolutional neural network, collect laboratory data, perform data preprocessing, obtain normalized sample set, use multi-dimensional convolutional neural network to deeply mine hidden fault information, and then according to the model Predict the difference between the resolution result and the manual label corresponding to the measured laboratory data to adjust the internal weight parameters of the fault diagnosis model. The accuracy rate is further improved. The conclusion obtained by the present invention is accurate, and can timely discover and give early warning to potential safety hazards, so as to prevent the occurrence of safety accidents.

Description

technical field [0001] The invention relates to the field of artificial intelligence, in particular to an experiment environment safety early warning method based on a multidimensional convolutional neural network. Background technique [0002] As the scale and number of laboratories continue to increase, from learners, technicians to professional managers, their operational proficiency and safety awareness are uneven, which makes laboratory management face more and more problems. At present, the safety supervision of laboratories in various fields still adopts a purely manual management model. To enter the laboratory, you only need to make an appointment to register and use it. The use process and operation details are difficult to manage. Cases of equipment damage are not uncommon. Therefore, in addition to safety awareness education, intelligent management of laboratories is the most reliable way to avoid safety hazards, so as to prevent problems before they happen. [0...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G08B21/18G06Q50/20G06N3/04
CPCG08B21/18G06Q50/205G06N3/045
Inventor 谭立国宋申民李君宝鄂鹏王晓野
Owner 哈尔滨工业大学人工智能研究院有限公司