机器学习算法在频率调度优化中的应用

Application of Machine Learning Algorithm in Frequency Scheduling Optimization

本文介绍了机器学习方法中常用的一些聚类方法的基本原理和机制。基于当前频率调度的基本流程,调度中存在播出需求任务大量增加、需求频率不均匀、设备相对不足等问题。为解决此问题,本文借助机器学习、数据挖掘等人工智能技术建立数学模型,不断调整模型参数,对大量的数据进行深度挖掘,找出其中潜在的规律,优化调度流程,从而实现频率调度的精准化,提升发射资源的利用率。

This article introduces the basic principles and mechanisms of some commonly used clustering methods in machine learning.Based on the basic process of current frequency scheduling,there are problems in scheduling,such as a significant increase in broadcast demand tasks,uneven demand frequency,and relatively insufficient equipment.To solve the problem,this article utilizes artificial intelligence technologies such as machine learning and data mining to establish a mathematical model,continuously adjust model parameters,deeply mine a large amount of data,identify potential patterns,optimize the frequency scheduling algorithm,and achieve precision in frequency scheduling,thereby improving the utilization of transmission resources.