基于多模態(tài)深度學(xué)習(xí)的新能源設(shè)備運(yùn)維決策研究

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中圖分類號：TP277 文獻(xiàn)標(biāo)志碼：ADOI：10.19968/j.cnki.hnkj.1003-5168.2025.12.004

文章編號：1003-5168（2025）12-0020-04

Research on Operation and Maintenance Decision-Making of New Energy Equipment Based on Multimodal Deep Learning

ZHAO JingleiLI YaoxiWUXuejie LIUMingyueYIN Haolin （PowerChina Renewable EnergyCo.，Ltd.，Beijing1Oo101，China）

Abstract：[Purposes] New energy equipment is often exposed to complex environments and is prone to secondary damage due to various factors，leading to a decrease in the quality of maintenance decisions for new energy equipment. Therefore，a new energy equipment maintenance decision method based on multimodal deep learning is proposed.[Methods] By using multimodal fusion technology，the operation and maintenance data of different modalities such as text， image，language， etc.are organically integrated to form a consistent reference framework and scale range.Build a new energy equipment operation and maintenance decision model based on multimodal deep learning，input the fused operation and maintenance data into the model，and predict the potential failure risks of the equipment through real-time analysis of its operating status. Generate new energy equipment operation and maintenance dispatch decisions to determine whether to dispatch operation and maintenance personnel for repair and maintenance. [Findings] The case analysis results show that the proposed method can accurately predict new energy equipment failures and generate better results in the operation and maintenance decisions of new energy equipment.[Conclusions] This method has practical application value and can provide certain degree of data support for new energy equipment operation and maintenance decisions.

Keywords： multimodal; deep learning; new energy; equipment operation and maintenance; decisionmaking methods

0 引言

新能源主要包括太陽能、風(fēng)能、水能等天然形成的資源，能夠極大代替石油、煤炭等資源，減少環(huán)境污染，改善生活環(huán)境。（剩余3702字）