基于聚类轨迹优化机制的大数据网络社区人工智能评估算法  

Artificial in Telligence EvaluationAlgorithm of Big Data Network Community Based on Clustering Trajectory Optimization Mechanism

作　　者：骆舒萍[1] Luo Shuping(Experimental Training and Information Technology Center,Liming University,Quanzhou,Fujian 362000,China)

机构地区：[1]黎明职业大学实验实训与信息技术中心,福建泉州362000

出　　处：《伊犁师范大学学报（自然科学版）》2023年第3期55-61,共7页Journal of Yili Normal University：Natural Science Edition

基　　金：福建省十四五规划课题2022年度课题(FJJKGZ22-069).

摘　　要：为解决大数据部署过程中存在的一些不足,如难以迅速实现热点捕捉、聚类生存周期短、能耗较高等问题,提出了一种基于聚类轨迹优化机制的大数据网络社区人工智能评估算法.首先,算法针对网络聚类存在的社区性及热度性特点,基于矢量矩阵映射并采取信号抽样方式精确捕获节点对应的信号时域特征,以提高算法对网络节点热度的评估效果;随后,进一步采取积分映射方式按列重新排列信号空间的特征矢量,设计了基于特征矢量的网络热点显示方法,以增强算法对网络热点的捕获能力,进一步提升算法性能.仿真实验表明:与常用的多维检测评估算法邻域相似度评估算法相比,该算法具有聚合显影能力较强、评估失误率低等特点,具有很强的实际部署价值.In order to solve some shortcomings of the deployment process of big data,such as difficulty in quickly capturing hotspots,short clustering life cycle,and high energy consumption,a big data network community artificial intelligence evaluation algorithm based on clustering trajectory optimization mechanism is proposed.Firstly,the algorithm focuses on the community and heat characteristics of network clustering,and uses vector matrix mapping and signal sampling to accurately capture the time-domain features of signals corresponding to nodes,in order to improve the algorithm's evaluation of network node heat.Subsequently,an integral mapping method was further adopted to rearrange the feature vectors of the signal space by column,and a network hotspot display method based on feature vectors was designed to enhance the algorithm's ability to capture network hotspots and further improve its performance.Simulation experiments show that compared with commonly used multi-dimensional detection and evaluation algorithms for neighborhood similarity evaluation,the algorithm proposed in this paper has advantages such as better aggregation and development performance,low evaluation error rate,and strong practical deployment value.

关 键 词：移动大数据 聚类匹配 矢量矩阵 时域特征 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]