Internet of Things unstructured big data analysis algorithm based on machine learning

Abstract

The invention discloses an Internet of Things unstructured big data analysis algorithm based on machine learning. The algorithm comprises the following steps of: 1), constructing a big data platform,and acquiring unstructured original data input by an online client at a front end; 2) according to the unstructured original data received by the front end, preprocessing the unstructured original data by a client terminal background so as to perform background data mining; 3) removing redundant and noisy junk data of the unstructured original data to obtain an unstructured data sample; and 4) clustering unstructured data sample by the Internet of Things unstructured big data analysis algorithm based on machine learning, and reasoning and training a prediction model, then performing predictionwith the trained prediction model, and outputting the prediction result. The method can realize analysis of the Internet of Things unstructured big data by utilizing the algorithm based on machine learning.

Description

technical field [0001] The invention relates to an unstructured big data analysis algorithm of the Internet of Things, in particular to a machine learning-based unstructured big data analysis algorithm of the Internet of Things. Background technique [0002] Machine learning is a research hotspot in computer science and artificial intelligence. The industry does not have a unified standard for defining "machine learning", but machine learning is generally a model of human cognitive processes and learning processes, combined with the computing power of a computer to perform human behavior simulations. Acquire new knowledge or skill algorithms. It uses prior knowledge and training data to guide learning, and continuously adjusts the existing knowledge structure to improve its performance. In recent years, many machine learning algorithms have been widely used in engineering practice and scientific research. Such as clustering (data clustering), SVM (support vector machine),...

AI Technical Summary

Problems solved by technology

High-dimensional feature extraction involves the problems of "distance" and "dimensionality reduction". The ideal feature extraction algorithm has a low measurement value for the degree of distance preservation. However, there is no public precedent for applying machine learning to the field of big data in the prior art. That is, there is no public machine learning-based IoT unstructured big data analysis solution

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