摘要
仓储设备智能化进程中存在着故障诊断准确率不高和设备调度效率低下的问题。本文针对这些问题,提出利用AI人工智能算法中的朴素贝叶斯算法,采集仓储设备的运行数据,并通过特征提取和概率计算构建一个高效的故障诊断模型,以提升故障检测的准确性。同时,结合设备使用情况和历史数据制定了最优设备调度方案,旨在提高仓储作业的效率。研究结果表明,采用朴素贝叶斯算法能够显著增强仓储设备的智能诊断和调度能力,为仓储管理提供更加智能和高效的解决方案。
There are still problems with low accuracy in fault diagnosis and low efficiency in equipment scheduling in the process of intelligent storage equipment.This article proposes to use the Naive Bayes algorithm in AI artificial intelligence algorithms to collect operational data of storage equipment,and construct an efficient fault diagnosis model through feature extraction and probability calculation to improve the accuracy of fault detection.At the same time,based on equipment usage and historical data,the optimal equipment scheduling plan has been developed to improve the efficiency of warehousing operations.The research results indicate that using Naive Bayes algorithm can significantly enhance the intelligent diagnosis and scheduling capabilities of warehouse equipment,and provide more intelligent and efficient solutions for warehouse management.
作者
景奕昕
JING Yixin(Wuhan IPASON Technology Co.,Ltd.,Wuhan Hubei 430000)
出处
《软件》
2024年第10期109-111,共3页
Software
关键词
人工智能
仓储设备
智能化管理
artificial intelligence
storage equipment
intelligent management
