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传统的物联网大数据信息过滤方法由于忽略了对大数据特征的提取,过滤效果和去噪处理对仿真结果的影响,导致过滤精度低、性能差。提出基于支持向量机(SVM)的物联网大数据有效信息过滤挖掘算法。首先,进行了物联网大数据系统模型构建与特征提取,对有效信息特征进行了关联维特征提取预处理,对有效信息数据的关联因子进行排序,提取关联度主特征量,设计滤波器非关联信息进行合理过滤,对所有的数据进行规整处理,转换到相同的区间进行处理,实现数据规约,基于支持向量机SVM算法实现数据有效信息特征挖掘。仿真结果表明,采用该算法进行大数据有效信息过滤,精度较高,性能优越,展示了较好的应用价值。
The traditional IOT big data message filtering method ignores the impact of big data feature extraction, filtering and denoising processing on the simulation results, resulting in low filtering accuracy and poor performance. This paper proposes an Internet of Things big data effective information filtering algorithm based on Support Vector Machine (SVM). Firstly, the construction and feature extraction of big data system in Internet of Things (IoT) are carried out. Preprocessing of feature extraction is carried out on the effective information features. The correlation factors of effective information data are sorted. The main features of correlation are extracted. Information is reasonably filtered, all the data are processed regularly, converted to the same interval for processing, and the data protocol is implemented. Based on the support vector machine SVM algorithm, the data effective information feature mining is realized. The simulation results show that this algorithm can filter the effective information of big data with high precision and superior performance, which shows the good application value.