Simulated analysis method of classification optimizing model for temperature-sensing big data of intelligent building

Abstract

The invention relates to a simulated analysis method of a classification optimizing model for temperature-sensing big data of an intelligent building. A classification model of the temperature-sensing big data based on chaotic difference disturbance fuzzy C mean-value clustering is provided, a distribution structure model of the temperature-sensing big data of the intelligent building in a database storage system needs to be analyzed, and characteristic fusion and time sequence analysis are carried out on big-data information flows. Chaotic difference disturbance is introduced on the basis of traditional fuzzy C mean-value clustering, the classification process is prevented from local convergence and local optimizing, and the data clustering performance is improved. A big data classification method is used, the classification error rate of the temperature data of the intelligent building is effectively reduced, and the convergence and accuracy of data classification are higher.

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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