The rapid economic development has become the theme of today’s social development. With the rapid development of Internet technology, the amount of information has shown an explosive growth. While facing busy work every day, people also need to face a very large amount of data. A more precise expression means that a large amount of data storage space and a large amount of redundant data copies are needed. This paper mainly studies the algorithm design of artificial intelligence technology in edge cloud computing edge node storage optimization algorithm. The user submits a virtual machine request, and the constraint optimization algorithm allocates the request to a suitable server for execution according to the related information of the virtual machine request submitted by the user and the use of data center server resources, and combines the virtual machine's artificial intelligence data mining technology to minimize a large number of servers meet user requests, thereby ultimately achieving the goal of reducing energy consumption in edge cloud computing data centers. Experimental data shows that the analysis and positioning optimization network has an absolute impact on the overall performance of the detection and recognition network. When the score threshold is 7 and 8, the MAP improvement effect is the greatest. Experimental results show that artificial intelligence technology can reduce the energy consumption of edge cloud computing data centers.

经济的快速发展已成为当今社会发展的主题。随着Internet技术的飞速发展，信息量呈爆炸性增长。人们每天面对繁忙的工作时，还需要面对大量数据。更精确的表达意味着需要大量的数据存储空间和大量的冗余数据副本。本文主要研究边缘云存储边缘节点存储优化算法中人工智能技术的算法设计。用户提交虚拟机请求，约束优化算法根据用户提交的虚拟机请求的相关信息以及数据中心服务器资源的使用情况，将请求分配给合适的服务器执行。并结合了虚拟机的人工智能数据挖掘技术，以最大程度地减少满足用户请求的大量服务器，从而最终实现减少边缘云计算数据中心能耗的目标。实验数据表明，分析定位优化网络对检测识别网络的整体性能具有绝对的影响。当分数阈值为7和8时，MAP改善效果最大。实验结果表明，人工智能技术可以降低边缘云计算数据中心的能耗。从而最终达到减少边缘云计算数据中心能耗的目标。实验数据表明，分析定位优化网络对检测识别网络的整体性能具有绝对的影响。当分数阈值为7和8时，MAP改善效果最大。实验结果表明，人工智能技术可以降低边缘云计算数据中心的能耗。从而最终达到减少边缘云计算数据中心能耗的目标。实验数据表明，分析定位优化网络对检测识别网络的整体性能具有绝对的影响。当分数阈值为7和8时，MAP改善效果最大。实验结果表明，人工智能技术可以降低边缘云计算数据中心的能耗。