International Open Access Journal Platform

AI赋能的MoE油库高效并行风险预测系统设计与实现

Design and Implementation of an AI-empowered Efficient Parallel Risk Prediction System for MoE oil Depots

Environment and Resource / 2024,6(4): 121-128 / 2025-03-07 94 185

View Download PDF

• Authors: 张韶田¹      何俊霖¹      王一婷¹      黄李元钧²      李满弈¹     
• Information:

  1. 重庆科技大学石油与天然气工程学院，重庆；
  2. 重庆科技大学电子智能材料与器件研究中心，重庆

• Keywords: Artificial intelligence; Hybrid Expert Model (MoE); Oil depot safety management; Risk prediction; Efficient parallelism 人工智能；混合专家模型（MoE）；油库安全管理；风险预测
• Abstract: With the rapid development of artificial intelligence technology, its application in the industrial field is becoming increasingly widespread. As an important facility for energy storage and transportation, the safety management of oil depots is directly related to national energy security and environmental protection. This article aims to explore how to use a hybrid expert model (MoE) and artificial intelligence technology to design and implement an efficient parallel risk prediction system to improve the safety management level of oil depots. The system can identify and warn potential risks in advance through real-time data collection, intelligent analysis, and prediction, providing strong support for emergency response and safety management of oil depots. 随着人工智能技术的飞速发展，其在工业领域的应用日益广泛。油库作为能源储存和转运的重要设施，其安全管理直接关系到国家能源安全和环境保护。本文旨在探讨如何利用混合专家模型（MoE）与人工智能技术，设计并实现一个高效并行的风险预测系统，以提升油库的安全管理水平。该系统通过实时数据采集、智能分析与预测，能够提前识别并预警潜在风险，为油库的应急响应和安全管理提供有力支持。
• DOI: https://doi.org/10.35534/er.0604011
• Cite: 张韶田，何俊霖，王一婷，等．AI赋能的MoE油库高效并行风险预测系统设计与实现［J］．环境与资源，2024，6（4）：121-128．