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| Auxiliary decision-making technology for upgrading concentrator equipment group based on parallel image and deep learning |
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Received:June 02, 2022
Revised:July 21, 2022
Accepted:August 29, 2022
Published Online:June 25, 2023
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| DOI： |
| KeyWord:Parallel images; Deep learning; Concentrator equipment group; Upgrade the auxiliary decision-making technology; Hierarchical decision-making; |
| Author | Institution |
| Yang Xiuqiao |
Guangxi Zhaoping Zhaojin Mining Co., Ltd. |
| Lei Yu |
Guangxi Geological Exploration Institute of China General Administration of Metallurgical Geology |
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| Abstract: |
| The existing auxiliary decision-making technology for concentrator equipment upgrading has poor precision in the reverse fine-tuning result of neural network, resulting in poor upgrading and optimization effect of concentrator equipment. Therefore, the auxiliary decision-making technology for concentrator equipment group upgrading is designed based on parallel image and deep learning algorithm. Using parallel images, collect and render the equipment information of the concentrator, mark the semantic accuracy style, and calculate the corrected image loss function; The parameters of decision-making model are trained by deep learning, the activation function of random unit is obtained, and the reconstruction coefficient of cycle times and error is obtained; An auxiliary decision-making model for upgrading of concentrator equipment group is established. The experimental results show that this method can get the decision results at the fastest speed with less error, and the data accuracy and integrity of the upgrading process of concentrator equipment group can be guaranteed. |
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