Simulation analysis method for classification optimization model of temperature sensing big data in intelligent buildings

Abstract

The invention relates to a classification optimization model simulation analysis method for temperature sensing big data in intelligent buildings, and proposes a classification model for temperature sensing big data based on chaotic differential disturbance fuzzy C-means clustering, which needs to analyze the temperature sensing big data in intelligent buildings. The distributed structure model of data in the database storage system performs feature fusion and time series analysis on the information flow of big data. On the basis of traditional fuzzy C-means clustering processing, chaotic differential disturbance is introduced to avoid falling into local classification during the classification process. Convergence and local optimization improve data clustering performance. The present invention adopts the big data classification method, effectively reduces the misclassification rate of temperature data in intelligent buildings, and has high convergence and accuracy of data classification.

Description

technical field [0001] The invention relates to the field of big data, in particular to a classification optimization model simulation analysis method for temperature sensing big data in an intelligent building. Background technique [0002] With the development of multi-mode control technology and artificial intelligence technology, intelligent temperature control technology is widely used in intelligent buildings. Through intelligent temperature control, adaptive temperature adjustment is performed in intelligent buildings to achieve energy saving and environmental protection, which can improve the comfort of the human body in the building. . In intelligent buildings, the temperature of each building area and module is collected through the temperature sensor network, and the collected temperature data is adaptively processed. Through data mining and data classification technology, the temperature attributes of each building area can be analyzed. The air conditioning cont...

AI Technical Summary

Problems solved by technology

The data clustering method is used to sample and analyze temperature sensing big data in intelligent buildings to realize intelligent temperature control. In traditional methods, the classification algorithms for big data mainly include K-Means clustering algorithm, fuzzy C-means clustering algorithm, and decision tree Classification algorithm and particle swarm classification algorithm, etc. Among them, the fuzzy C-means clustering algorithm is the most common algorithm, but this algorithm has poor anti-interference performance on the disturbance difference vector in the big data classification, and the calculation cost is too large, so it is difficult to realize real-time temperature Analysis and processing of sensory big data

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